Identifying Clusters in High Dimensional Data

Identifying Clusters in naughty balanceal selective breedingAsk those who remember, atomic number 18 mindful if you do non know). (Holy Quran, 643) removal Of Redundant Dimensions To Find Clusters In N-Dimensional selective in embodimentation Using Sub station ClusteringAbstractThe entropy excavation has emerged as a powerful tool to extract fellowship from huge selective readingbases. Researchers have introduced several(pre noun phrase) machine development algorithms to explore the infobases to d sensitive culture, cabalistic patterns, and rules from the entropy which were non known at the teaching recording duration. cod to the remarkable developments in the computing device storage capacities, plowing and powerful algorithmic tools, practiti wizrs be developing new and improved algorithms and proficiencys in several argonas of entropy tap to discoer the rules and relationship among the attributes in b are(a) and complex laster dimensional entrop ybases. much(prenominal) than everyplace entropy minelaying has its implementation in large variety of areas ranging from banking to merchandising, engineering to bioinformatics and from investiture to chance analysis and player detection. Practiti sensationrs are analyzing and implementing the proficiencys of artificial neural networks for gradeification and regression jobs beca usance of accuracy, efficiency. The aim of his short research project is to develop a centering of identifying the meets in in high spirits dimensional information as wholesome as unnecessary dimensions which give the axe create a noise in identifying the clusters in high dimensional selective information. Techniques use in this project utilizes the strength of the projections of the information full evens on the dimensions to identify the intensity of projection along each dimension in severalize to breakthrough cluster and tautological dimension in high dimensional info.1 Introd uctionIn numerous scientific settings, engineering handlees, and business applications ranging from experimental sensing element data and cultivate control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and sto departure. Whereas sensor equipments as well as big storage devices are entranceting cheaper day by day, data analysis tools and techniques wrap behind. Clustering modes are mutual solutions to unsupervised learning problems where incomplete any expert familiarity nor some assistantful annotation for the data is available. In general, bunch groups the data objects in a way that standardized objects get together in clusters whereas objects from distinct clusters are of high dissimilarity. even it is observed that b totally disclose al close no organise even it is known thither must be groups of similar objects. In some cases, the reason is that the cluster struct ure is stimulated by some subsets of the spaces dimensions entirely, and the many additional dimensions contribute cryptograph former(a) than making noise in the data that hinder the denudation of the clusters at bottom that data. As a solution to this problem, clump algorithms are applied to the pertinent subspaces merely. Immediately, the new question is how to visualize the relevant subspaces among the dimensions of the climb space. Being faced with the power set of the set of dimensions a tool force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality.In high dimensional data, as dimensions are increasing, the visual percept and prototype of the data becomes to a great extent difficult and sometimes increase in the dimensions butt create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersi ng towards the corners / dimensions. Subspace assemble solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which green goddess be marked as redundant in high dimensional data. It in any case solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace clustering is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the swearword of dimensionality occurs. The nearly of the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results base on the revision in which the input signal record s were formed 2.Subspace clustering quite a little identify the incompatible cluster within subspaces which exists in the huge amount of exchanges data and through it we chamberpot find which of the different attributes are associate. This great deal be usable in promoting the sales and in planning the inventory levels of different products. It support be use for finding the subspace clusters in spacial databases and some useful closes displace be taken based on the subspace clusters identified 2. The technique employ here for indentifying the redundant dimensions which are creating noise in the data in install to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all practical combinations among all no. of dimensions and at long last the union of all projection a long all dimensions and crushd, it leave show the contribution of each dimension in indentifying the cluster which forget be equal by the encumbrance of projection. If any of the given dimension is contributing genuinely less in order to building the weight of projection, that dimension stinkpot be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this outline will be covered in later chapters.2 selective information tap2.1 What is information tap?Data archeological site is the process of analyzing data from different office and summarizing it for getting useful information. The information keister be utilise for many useful purposes give care increasing revenue, cuts costs etc. The data digging process also finds the hidden knowledge and relationship within the data which was not known patch data recording. Describing the data is the outgrowth step in data mining, followed by summarizing its attributes (like standard dispute mean etc). After that data is reviewed using visual tools like charts and graphs and so meaningful relations are sicd. In the data mining process, the steps of fooling, exploring and selecting the sound data are critically important. User enkindle analyze data from different dimensions categorize and iterate it. Data mining finds the correlation or patterns amongst the palm in large databases.Data mining has a great potential to help companies to focus on their important information in their data storage warehouse. It asshole predict the future trends and styles and allows the business to make more proactive and knowledge driven decisions. It fucking answer the business questions that were traditionally much time go through to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is ordinarily used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques sess be implemented on existing platforms for enhance the value of information resources. Data mining tools base analyze coarse databases to deliver answers to the questions.Some other terms contains similar meaning from data mining such as fellowship mining or intimacy Extraction or Pattern Analysis. Data mining faeces also be treated as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps.* Data cleaning (removing the noise and inconsistent data)* Data Integration (combining multiple data sources)* Data selection (retrieving the data relevant to analysis task from database)* Data Transformation (tran sforming the data into appropriate forms for mining by performing summary or aggregation operations)* Data mining (applying the intelligent methods in order to extract data patterns)* Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures)* Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the exploiter)2.2 DataData gage be any fictional character of facts, or text, or ikon or number which send word be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and depend data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions .2.3 InformationThe information can be retrieved from the data via patterns, associations or relationship may exist in the data. For exercising the sell point of sale transaction data can be analyzed to yield information about the products which are being s hoary and when.2.4 KnowledgeKnowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can countenance the knowledge vitiateing behavior of customer. Hence items which are at most risk of infection for promotional efforts can be determined by manufacturer easily.2.5 Data warehouseThe advancement in data capture, affect power, data transmission and storage technologies are enabling the industry to integrate their heterogeneous databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data w arehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an intellectionl way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user rag and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse.Data mining is primarily used today by companies with a secure consumer focus retail, financial, communication, and marketing organizations. It allows these organizations to evaluate associations between certain internal external genes. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to regard detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities.Data mining unremarkably automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered instantly from the data very diligently. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to distinguish the targets most probably to increase return on investment as maximum as thinkable in future mailings. Tools used in data mining traverses through huge databases and discover antecedently unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are ordinarily purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent assurance card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analyze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. abundant databases, in turn, give improved and better predictions.2.6 Descriptive and Predictive Data MiningDescriptive data mining aims to find patterns in the data that depart some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mi ning is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a sample can be made that predicts which prospects are most seeming to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network deterrent example or set of mildews that can be used to predict some response of interest. For example, a credit card company may want to take in in predictive data mining, to derive a (trained) model or set of models that can quickly identify transactions which have a high probability of being fraudulent. former(a) types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and dubitable methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks.* Data facility* Data Reduction* Data Modeling and Prediction* Case and Solution Analysis2.7 textual matter MiningThe Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived lingual features and the removal of others, and subsequent insertion into a database), deriving patterns within the structured data, and finally evaluation and interpretation of the output. The High persona in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of r ough taxonomies, entity relation modeling, text file summarisation can be include as text mining tasks.Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different scripted resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional shipway of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from lifelike language text preferably of from structured databases of facts. Databases are designed and developed for programs to execute automatically text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that pro grams that read the way people do could be written.2.8 electronic network MiningWeb Mining is the technique which is used to extract and discover the information from sack up documents and hunt automatically. The interest of various research communities, tremendous growth of information resources on Web and late(a) interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks.* election finding fetching intend meshwork documents.* Information selection and pre-processing selecting and preprocessing specific information from fetched blade resources automatically.* Generalization automatically discovers general patterns at individualistic and across multiple website* Analysis reasonableation and explanation of mined patterns.Web Mining can be mainly categorized into three areas of interest based on which part of Web inescapably to be mined Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mini ng describes the discovery of useful information from the web contents, data and documents 10. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyper tie in. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on bequest systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over the Web. Users are accessing the applications over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, be get to internet is capable of making joining to any oth er computer anywhere in the world 11. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or underground data can fall in this area. Unstructured data such as free text or cheat structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is loosely found in the web contents. The work on Web content mining is mostly make from 2 point of views, one is IR and other is DB point of view. From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and integrates them so that the more sophisticated queries other than keywords could be performed. 10.In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inte r document structure 10. It is closely related to the web use mining 14. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it 14. Based on web morphologic data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural component that links or connects the web page to a different web page or different location. The other kind of the web structure mining i nteracts with the document structure, which is using the shoe channelize-like structure to analyze and describe the HTML or XML tags within the web pages.With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. 13. The Web physical exertion mining interacts with data generated by users clickstream. The web usage data includes web host access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction 10. So the web usage mining is the most important task of the web mining 12. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log record s are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanced and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and portentous information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages data collection and pre-processing, pattern discovery, and pattern analysis 13. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents th e activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations are performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users.3 Classification 3.1 What is Classification?As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration 16. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use sorting to guess whether the weather condition on a specific day would be sunny, cloudy or rainy. The data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one clas s to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. last Trees and Bayesian Networks are the examples of classification methods. wizard type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of outer space measures or on any other parameter, depending upon the need and the given data.Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Cl assification is a way of thinking about things rather than a study of things itself so it draws its speculation and application from complete range of human experiences and thoughts 18. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing classification from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. thither are many techniques which are used for data categorization / classification. The most common are conclusiveness direct classifier and Bayesian classifiers.3.2 Types of ClassificationThere are two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discove ring a function from upbringing data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised savant is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way.The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown examples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques t hat are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source zone based on self-reliant Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are purpose Trees classifiers and Nave Bayesian Classifiers.3.2.1 Decision Trees ClassifierThere are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well 16. Tree like graph or decisions models and their poss ible consequences including resource costs, find out event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing classifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Decision trees are simple but powerful form of multiple variable analyses 15. Classification is done by tree like structures that have different test criteria for a va riable at each of the lymph glands. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset 15.In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called shank node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value against some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to r epresent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via distinguishable set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distan ce measures among each other.Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree.Roughly, the idea is based upon the number of telephone line items we have to make different decisions. If we dont have much, you get at any cost. If you have a lot of items then you only buy if it is inexpensive. directly if commonplace items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 otherwise no need to buy too expensive items. So in this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving.The root node at the top of the tree structure is covering the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then on that point is again high risk involved while if family car is used then there is low risk involved.In the field of sciences engineering and in the applied areas including business tidings and data mining, many useful features are being introduced as the result of evolution of decision trees.* With the help of transformation in decision trees, the volume of data can be reduced into more compact form that preserves the major characteristicIdentify ing Clusters in High Dimensional DataIdentifying Clusters in High Dimensional DataAsk those who remember, are mindful if you do not know). (Holy Quran, 643)Removal Of Redundant Dimensions To Find Clusters In N-Dimensional Data Using Subspace ClusteringAbstractThe data mining has emerged as a powerful tool to extract knowledge from huge databases. Researchers have introduced several machine learning algorithms to explore the databases to discover information, hidden patterns, and rules from the data which were not known at the data recording time. Due to the remarkable developments in the storage capacities, processing and powerful algorithmic tools, practitioners are developing new and improved algorithms and techniques in several areas of data mining to discover the rules and relationship among the attributes in simple and complex higher dimensional databases. Furthermore data mining has its implementation in large variety of areas ranging from banking to marketing, engineering to bioinformatics and from investment to risk analysis and fraud detection. Practitioners are analyzing and implementing the techniques of artificial neural networks for classification and regression problems because of accuracy, efficiency. The aim of his short research project is to develop a way of identifying the clusters in high dimensional data as well as redundant dimensions which can create a noise in identifying the clusters in high dimensional data. Techniques used in this project utilizes the strength of the projections of the data points along the dimensions to identify the intensity of projection along each dimension in order to find cluster and redundant dimension in high dimensional data.1 IntroductionIn numerous scientific settings, engineering processes, and business applications ranging from experimental sensor data and process control data to telecommunication traffic observation and financial transaction monitoring, huge amounts of high-dimensional measurement data are produced and stored. Whereas sensor equipments as well as big storage devices are getting cheaper day by day, data analysis tools and techniques wrap behind. Clustering methods are common solutions to unsupervised learning problems where neither any expert knowledge nor some helpful annotation for the data is available. In general, clustering groups the data objects in a way that similar objects get together in clusters whereas objects from different clusters are of high dissimilarity. However it is observed that clustering disclose almost no structure even it is known there must be groups of similar objects. In many cases, the reason is that the cluster structure is stimulated by some subsets of the spaces dimensions only, and the many additional dimensions contribute nothing other than making noise in the data that hinder the discovery of the clusters within that data. As a solution to this problem, clustering algorithms are applied to the relevant subspaces only. Immediately, the new question is how to determine the relevant subspaces among the dimensions of the full space. Being faced with the power set of the set of dimensions a brute force trial of all subsets is infeasible due to their exponential number with respect to the original dimensionality.In high dimensional data, as dimensions are increasing, the visualization and representation of the data becomes more difficult and sometimes increase in the dimensions can create a bottleneck. More dimensions mean more visualization or representation problems in the data. As the dimensions are increased, the data within those dimensions seems dispersing towards the corners / dimensions. Subspace clustering solves this problem by identifying both problems in parallel. It solves the problem of relevant subspaces which can be marked as redundant in high dimensional data. It also solves the problem of finding the cluster structures within that dataset which become apparent in these subspaces. Subspace cluster ing is an extension to the traditional clustering which automatically finds the clusters present in the subspace of high dimensional data space that allows better clustering the data points than the original space and it works even when the curse of dimensionality occurs. The most of the clustering algorithms have been designed to discover clusters in full dimensional space so they are not effective in identifying the clusters that exists within subspace of the original data space. The most of the clustering algorithms produces clustering results based on the order in which the input records were processed 2.Subspace clustering can identify the different cluster within subspaces which exists in the huge amount of sales data and through it we can find which of the different attributes are related. This can be useful in promoting the sales and in planning the inventory levels of different products. It can be used for finding the subspace clusters in spatial databases and some useful d ecisions can be taken based on the subspace clusters identified 2. The technique used here for indentifying the redundant dimensions which are creating noise in the data in order to identifying the clusters consist of drawing or plotting the data points in all dimensions. At second step the projection of all data points along each dimension are plotted. At the third step the unions of projections along each dimension are plotted using all possible combinations among all no. of dimensions and finally the union of all projection along all dimensions and analyzed, it will show the contribution of each dimension in indentifying the cluster which will be represented by the weight of projection. If any of the given dimension is contributing very less in order to building the weight of projection, that dimension can be considered as redundant, which means this dimension is not so important to identify the clusters in given data. The details of this strategy will be covered in later chapter s.2 Data Mining2.1 What is Data Mining?Data mining is the process of analyzing data from different perspective and summarizing it for getting useful information. The information can be used for many useful purposes like increasing revenue, cuts costs etc. The data mining process also finds the hidden knowledge and relationship within the data which was not known while data recording. Describing the data is the first step in data mining, followed by summarizing its attributes (like standard deviation mean etc). After that data is reviewed using visual tools like charts and graphs and then meaningful relations are determined. In the data mining process, the steps of collecting, exploring and selecting the right data are critically important. User can analyze data from different dimensions categorize and summarize it. Data mining finds the correlation or patterns amongst the fields in large databases.Data mining has a great potential to help companies to focus on their important info rmation in their data warehouse. It can predict the future trends and behaviors and allows the business to make more proactive and knowledge driven decisions. It can answer the business questions that were traditionally much time consuming to resolve. It scours databases for hidden patterns for finding predictive information that experts may miss it might lies beyond their expectations. Data mining is normally used to transform the data into information or knowledge. It is commonly used in wide range of profiting practices such as marketing, fraud detection and scientific discovery. Many companies already collect and refine their data. Data mining techniques can be implemented on existing platforms for enhance the value of information resources. Data mining tools can analyze massive databases to deliver answers to the questions.Some other terms contains similar meaning from data mining such as Knowledge mining or Knowledge Extraction or Pattern Analysis. Data mining can also be trea ted as a Knowledge Discovery from Data (KDD). Some people simply mean the data mining as an essential step in Knowledge discovery from a large data. The process of knowledge discovery from data contains following steps.* Data cleaning (removing the noise and inconsistent data)* Data Integration (combining multiple data sources)* Data selection (retrieving the data relevant to analysis task from database)* Data Transformation (transforming the data into appropriate forms for mining by performing summary or aggregation operations)* Data mining (applying the intelligent methods in order to extract data patterns)* Pattern evaluation (identifying the truly interesting patterns representing knowledge based on some measures)* Knowledge representation (representing knowledge techniques that are used to present the mined knowledge to the user)2.2 DataData can be any type of facts, or text, or image or number which can be processed by computer. Todays organizations are accumulating large and growing amounts of data in different formats and in different databases. It can include operational or transactional data which includes costs, sales, inventory, payroll and accounting. It can also include nonoperational data such as industry sales and forecast data. It can also include the meta data which is, data about the data itself, such as logical database design and data dictionary definitions.2.3 InformationThe information can be retrieved from the data via patterns, associations or relationship may exist in the data. For example the retail point of sale transaction data can be analyzed to yield information about the products which are being sold and when.2.4 KnowledgeKnowledge can be retrieved from information via historical patterns and the future trends. For example the analysis on retail supermarket sales data in promotional efforts point of view can provide the knowledge buying behavior of customer. Hence items which are at most risk for promotional efforts can be de termined by manufacturer easily.2.5 Data warehouseThe advancement in data capture, processing power, data transmission and storage technologies are enabling the industry to integrate their various databases into data warehouse. The process of centralizing and retrieving the data is called data warehousing. Data warehousing is new term but concept is a bit old. Data warehouse is storage of massive amount of data in electronic form. Data warehousing is used to represent an ideal way of maintaining a central repository for all organizational data. Purpose of data warehouse is to maximize the user access and analysis. The data from different data sources are extracted, transformed and then loaded into data warehouse. Users / clients can generate different types of reports and can do business analysis by accessing the data warehouse.Data mining is primarily used today by companies with a strong consumer focus retail, financial, communication, and marketing organizations. It allows thes e organizations to evaluate associations between certain internal external factors. The product positioning, price or staff skills can be example of internal factors. The external factor examples can be economic indicators, customer demographics and competition. It also allows them to calculate the impact on sales, corporate profits and customer satisfaction. Furthermore it allows them to summarize the information to look detailed transactional data. Given databases of sufficient size and quality, data mining technology can generate new business opportunities by its capabilities.Data mining usually automates the procedure of searching predictive information in huge databases. Questions that traditionally required extensive hands-on analysis can now be answered directly from the data very quickly. The targeted marketing can be an example of predictive problem. Data mining utilizes data on previous promotional mailings in order to recognize the targets most probably to increase retur n on investment as maximum as possible in future mailings. Tools used in data mining traverses through huge databases and discover previously unseen patterns in single step. Analysis on retail sales data to recognize apparently unrelated products which are usually purchased together can be an example of it. The more pattern discovery problems can include identifying fraudulent credit card transactions and identifying irregular data that could symbolize data entry input errors. When data mining tools are used on parallel processing systems of high performance, they are able to analyze huge databases in very less amount of time. Faster or quick processing means that users can automatically experience with more details to recognize the complex data. High speed and quick response makes it actually possible for users to examine huge amounts of data. Huge databases, in turn, give improved and better predictions.2.6 Descriptive and Predictive Data MiningDescriptive data mining aims to fin d patterns in the data that provide some information about what the data contains. It describes patterns in existing data, and is generally used to create meaningful subgroups such as demographic clusters. For example descriptions are in the form of Summaries and visualization, Clustering and Link Analysis. Predictive Data Mining is used to forecast explicit values, based on patterns determined from known results. For example, in the database having records of clients who have already answered to a specific offer, a model can be made that predicts which prospects are most probable to answer to the same offer. It is usually applied to recognize data mining projects with the goal to identify a statistical or neural network model or set of models that can be used to predict some response of interest. For example, a credit card company may want to engage in predictive data mining, to derive a (trained) model or set of models that can quickly identify transactions which have a high proba bility of being fraudulent. Other types of data mining projects may be more exploratory in nature (e.g. to determine the cluster or divisions of customers), in which case drill-down descriptive and tentative methods need to be applied. Predictive data mining is goad oriented. It can be decomposed into following major tasks.* Data Preparation* Data Reduction* Data Modeling and Prediction* Case and Solution Analysis2.7 Text MiningThe Text Mining is sometimes also called Text Data Mining which is more or less equal to Text Analytics. Text mining is the process of extracting/deriving high quality information from the text. High quality information is typically derived from deriving the patterns and trends through means such as statistical pattern learning. It usually involves the process of structuring the input text (usually parsing, along with the addition of some derived linguistic features and the removal of others, and subsequent insertion into a database), deriving patterns withi n the structured data, and finally evaluation and interpretation of the output. The High Quality in text mining usually refers to some combination of relevance, novelty, and interestingness. The text categorization, concept/entity extraction, text clustering, sentiment analysis, production of rough taxonomies, entity relation modeling, document summarization can be included as text mining tasks.Text Mining is also known as the discovery by computer of new, previously unknown information, by automatically extracting information from different written resources. Linking together of the extracted information is the key element to create new facts or new hypotheses to be examined further by more conventional ways of experimentation. In text mining, the goal is to discover unknown information, something that no one yet knows and so could not have yet written down. The difference between ordinary data mining and text mining is that, in text mining the patterns are retrieved from natural l anguage text instead of from structured databases of facts. Databases are designed and developed for programs to execute automatically text is written for people to read. Most of the researchers think that it will need a full fledge simulation of how the brain works before that programs that read the way people do could be written.2.8 Web MiningWeb Mining is the technique which is used to extract and discover the information from web documents and services automatically. The interest of various research communities, tremendous growth of information resources on Web and recent interest in e-commerce has made this area of research very huge. Web mining can be usually decomposed into subtasks.* Resource finding fetching intended web documents.* Information selection and pre-processing selecting and preprocessing specific information from fetched web resources automatically.* Generalization automatically discovers general patterns at individual and across multiple website* Analysis ve rification and explanation of mined patterns.Web Mining can be mainly categorized into three areas of interest based on which part of Web needs to be mined Web Content Mining, Web Structure Mining and Web Usage Mining. Web Contents Mining describes the discovery of useful information from the web contents, data and documents 10. In past the internet consisted of only different types of services and data resources. But today most of the data is available over the internet even digital libraries are also available on Web. The web contents consist of several types of data including text, image, audio, video, metadata as well as hyperlinks. Most of the companies are trying to transform their business and services into electronic form and putting it on Web. As a result, the databases of the companies which were previously residing on legacy systems are now accessible over the Web. Thus the employees, business partners and even end clients are able to access the companys databases over th e Web. Users are accessing the applications over the web via their web interfaces due to which the most of the companies are trying to transform their business over the web, because internet is capable of making connection to any other computer anywhere in the world 11. Some of the web contents are hidden and hence cannot be indexed. The dynamically generated data from the results of queries residing in the database or private data can fall in this area. Unstructured data such as free text or semi structured data such as HTML and fully structured data such as data in the tables or database generated web pages can be considered in this category. However unstructured text is mostly found in the web contents. The work on Web content mining is mostly done from 2 point of views, one is IR and other is DB point of view. From IR view, web content mining assists and improves the information finding or filtering to the user. From DB view web content mining models the data on the web and inte grates them so that the more sophisticated queries other than keywords could be performed. 10.In Web Structure Mining, we are more concerned with the structure of hyperlinks within the web itself which can be called as inter document structure 10. It is closely related to the web usage mining 14. Pattern detection and graphs mining are essentially related to the web structure mining. Link analysis technique can be used to determine the patterns in the graph. The search engines like Google usually uses the web structure mining. For example, the links are mined and one can then determine the web pages that point to a particular web page. When a string is searched, a webpage having most number of links pointed to it may become first in the list. Thats why web pages are listed based on rank which is calculated by the rank of web pages pointed to it 14. Based on web structural data, web structure mining can be divided into two categories. The first kind of web structure mining interacts with extracting patterns from the hyperlinks in the web. A hyperlink is a structural component that links or connects the web page to a different web page or different location. The other kind of the web structure mining interacts with the document structure, which is using the tree-like structure to analyze and describe the HTML or XML tags within the web pages.With continuous growth of e-commerce, web services and web applications, the volume of clickstream and user data collected by web based organizations in their daily operations has increased. The organizations can analyze such data to determine the life time value of clients, design cross marketing strategies etc. 13. The Web usage mining interacts with data generated by users clickstream. The web usage data includes web server access logs, proxy server logs, browser logs, user profile, registration data, user sessions, transactions, cookies, user queries, bookmark data, mouse clicks and scrolls and any other data as a result of interaction 10. So the web usage mining is the most important task of the web mining 12. Weblog databases can provide rich information about the web dynamics. In web usage mining, web log records are mined to discover the user access patterns through which the potential customers can be identified, quality of internet services can be enhanced and web server performance can be improved. Many techniques can be developed for implementation of web usage mining but it is important to know that success of such applications depends upon what and how much valid and reliable knowledge can be discovered the log data. Most often, the web logs are cleaned, condensed and transformed before extraction of any useful and significant information from weblog. Web mining can be performed on web log records to find associations patterns, sequential patterns and trend of web accessing. The overall Web usage mining process can be divided into three inter-dependent stages data collection and pre-proce ssing, pattern discovery, and pattern analysis 13. In the data collection preprocessing stage, the raw data is collected, cleaned and transformed into a set of user transactions which represents the activities of each user during visits to the web site. In the pattern discovery stage, statistical, database, and machine learning operations are performed to retrieve hidden patterns representing the typical behavior of users, as well as summary of statistics on Web resources, sessions, and users.3 Classification 3.1 What is Classification?As the quantity and the variety increases in the available data, it needs some robust, efficient and versatile data categorization technique for exploration 16. Classification is a method of categorizing class labels to patterns. It is actually a data mining methodology used to predict group membership for data instances. For example, one may want to use classification to guess whether the weather on a specific day would be sunny, cloudy or rainy. T he data mining techniques which are used to differentiate similar kind of data objects / points from other are called clustering. It actually uses attribute values found in the data of one class to distinguish it from other types or classes. The data classification majorly concerns with the treatment of the large datasets. In classification we build a model by analyzing the existing data, describing the characteristics of various classes of data. We can use this model to predict the class/type of new data. Classification is a supervised machine learning procedure in which individual items are placed in a group based on quantitative information on one or more characteristics in the items. Decision Trees and Bayesian Networks are the examples of classification methods. One type of classification is Clustering. This is process of finding the similar data objects / points within the given dataset. This similarity can be in the meaning of distance measures or on any other parameter, depe nding upon the need and the given data.Classification is an ancient term as well as a modern one since classification of animals, plants and other physical objects is still valid today. Classification is a way of thinking about things rather than a study of things itself so it draws its theory and application from complete range of human experiences and thoughts 18. From a bigger picture, classification can include medical patients based on disease, a set of images containing red rose from an image database, a set of documents describing classification from a document/text database, equipment malfunction based on cause and loan applicants based on their likelihood of payment etc. For example in later case, the problem is to predict a new applicants loans eligibility given old data about customers. There are many techniques which are used for data categorization / classification. The most common are Decision tree classifier and Bayesian classifiers.3.2 Types of ClassificationThere a re two types of classification. One is supervised classification and other is unsupervised classification. Supervised learning is a machine learning technique for discovering a function from training data. The training data contains the pairs of input objects, and their desired outputs. The output of the function can be a continuous value which can be called regression, or can predict a class label of the input object which can be called as classification. The task of the supervised learner is to predict the value of the function for any valid input object after having seen a number of training examples (i.e. pairs of input and target output). To achieve this goal, the learner needs to simplify from the presented data to hidden situations in a meaningful way.The unsupervised learning is a class of problems in machine learning in which it is needed to seek to determine how the data are organized. It is distinguished from supervised learning in that the learner is given only unknown e xamples. Unsupervised learning is nearly related to the problem of density estimation in statistics. However unsupervised learning also covers many other techniques that are used to summarize and explain key features of the data. One form of unsupervised learning is clustering which will be covered in next chapter. Blind source partition based on Independent Component Analysis is another example. Neural network models, adaptive resonance theory and the self organizing maps are most commonly used unsupervised learning algorithms. There are many techniques for the implementation of supervised classification. We will be discussing two of them which are most commonly used which are Decision Trees classifiers and Nave Bayesian Classifiers.3.2.1 Decision Trees ClassifierThere are many alternatives to represent classifiers. The decision tree is probably the most widely used approach for this purpose. It is one of the most widely used supervised learning methods used for data exploration. It is easy to use and can be represented in if-then-else statements/rules and can work well in noisy data as well 16. Tree like graph or decisions models and their possible consequences including resource costs, chance event, outcomes, and utilities are used in decision trees. Decision trees are most commonly used in specifically in decision analysis, operations research, to help in identifying a strategy most probably to reach a target. In machine learning and data mining, a decision trees are used as predictive model means a planning from observations calculations about an item to the conclusions about its target value. More descriptive names for such tree models are classification tree or regression tree. In these tree structures, leaves are representing classifications and branches are representing conjunctions of features those lead to classifications. The machine learning technique for inducing a decision tree from data is called decision tree learning, or decision trees. Dec ision trees are simple but powerful form of multiple variable analyses 15. Classification is done by tree like structures that have different test criteria for a variable at each of the nodes. New leaves are generated based on the results of the tests at the nodes. Decision Tree is a supervised learning system in which classification rules are constructed from the decision tree. Decision trees are produced by algorithms which identify various ways splitting data set into branch like segment. Decision tree try to find out a strong relationship between input and target values within the dataset 15.In tasks classification, decision trees normally visualize that what steps should be taken to reach on classification. Every decision tree starts with a parent node called root node which is considered to be the parent of every other node. Each node in the tree calculates an attribute in the data and decides which path it should follow. Typically the decision test is comparison of a value ag ainst some constant. Classification with the help of decision tree is done by traversing from the root node up to a leaf node. Decision trees are able to represent and classify the diverse types of data. The simplest form of data is numerical data which is most familiar too. Organizing nominal data is also required many times in many situations. Nominal quantities are normally represented via discrete set of symbols. For example weather condition can be described in either nominal fashion or numeric. Quantification can be done about temperature by saying that it is eleven degrees Celsius or fifty two degrees Fahrenheit. The cool, mild, cold, warm or hot terminologies can also be sued. The former is a type of numeric data while and the latter is an example of nominal data. More precisely, the example of cool, mild, cold, warm and hot is a special type of nominal data, expressed as ordinal data. Ordinal data usually has an implicit assumption of ordered relationships among the values. In the weather example, purely nominal description like rainy, overcast and sunny can also be added. These values have no relationships or distance measures among each other.Decision Trees are those types of trees where each node is a question, each branch is an answer to a question, and each leaf is a result. Here is an example of Decision tree.Roughly, the idea is based upon the number of stock items we have to make different decisions. If we dont have much, you buy at any cost. If you have a lot of items then you only buy if it is inexpensive. Now if stock items are less than 10 then buy all if unit price is less than 10 otherwise buy only 10 items. Now if we have 10 to 40 items in the stock then check unit price. If unit price is less than 5 then buy only 5 items otherwise no need to buy anything expensive since stock is good already. Now if we have more than 40 items in the stock, then buy 5 if and only if price is less than 2 otherwise no need to buy too expensive items. So i n this way decision trees help us to make a decision at each level. Here is another example of decision tree, representing the risk factor associated with the rash driving.The root node at the top of the tree structure is showing the feature that is split first for highest discrimination. The internal nodes are showing decision rules on one or more attributes while leaf nodes are class labels. A person having age less than 20 has very high risk while a person having age greater than 30 has a very low risk. A middle category a person having age greater than 20 but less than 30 depend upon another attribute which is car type. If car type is of sports then there is again high risk involved while if family car is used then there is low risk involved.In the field of sciences engineering and in the applied areas including business intelligence and data mining, many useful features are being introduced as the result of evolution of decision trees.* With the help of transformation in decis ion trees, the volume of data can be reduced into more compact form that preserves the major characteristic

Theories Relating To Gender Inequality

Theories Relating To Gender InequalityThere realize been a number of theories determine forward by various Institutions, Organizations, Authors, Scholars, Researchers, and Develop workforcet practitioners, several(prenominal)how to explain the problem wherefore the issue of charge upual practice varies from region to region and why imple manpowerting aro determinationual activity equality, and womanly empower handst is lower than expected in SSA. Amongst these theories atomic number 18 the Inequality and the Modernization possibleness used in this estimate to explain the wide sex activity problems animated in SSA. Borrowing from the words of John Martenussen, virtually of these theories study been propounded by Western and North Ameri earth-closet authors and save been termed growth and phylogeny theories. (Martenussen, 1997 p.51) As furthermost as this project is concern, I am going to use the parts of the theories that be relevant to the project.The Inequalit y surmiseThe origin of sexual activity distinction surrounded by men and women has been one of the most intellectual debates after the rise of modern feminism. Great thinkers in the news report of ideas some(prenominal)(prenominal) as Aristotle and Thomas Quinas suggested speculative definition of gender differences. Continuously, nineteenth century evolutionary theorist such as Bachofen and Karl Marx consider various possible evolutionary sequences in organization kinship and gender relations. Some early efforts aimed at justifying existing institutions and distinguishables to question them sound like contemporary standard. The aim tail end the origin of feminist analyses is the ideological implication of womanish mastery over the centuries. Also, in that respect deport been a high skipper prevalence of anthropoid status across time, space and kindly circumstances that argon beyond denial especially in SSA. Therefore the pervasiveness of male dominance is the a bsolute aim of analyzing gender differences. The question that boot outs is that how can the apparent universal subordination of womanish be reconciled with equality in SSA with it strong tralatitiousistic background? (Robert Marx Johnson 2005 p 30).Assumptions of the Inequality TheoryFirstly, Inequality possibility explains the biological difference mingled with men and women which is inescapable, amongst race, order, culture and tradition disregardless of existence developed or underdeveloped. According to Linsey 2007, sex is the biological difference amid men and women term gender is the social construction of sexes considering race, politics, social, economic, culture and traditional background. This cultures and traditions vary from place to place and from culture to culture. These cultures that are contemplateed change with time within and between cultures. (Linsey 2007, P 97)Following this sex distinction between male and female, some advanced societies (Western a nd North American societies) make tried to narrow down the gender gap by empowering females, by redefining laws and ignoring others to enhance outgrowth. That nonwithstanding, the distinction still persists and would always be there because no matter all the feminist analyses on sex and gender, humans would never revert nature on this perspective. Research realize proven that no amount of theorist thought process can subtle the unprejudiced fact of biological distinction, therefore variety would persistently exist no matter what. The question that ponders my mind is, why Sub-Saharan Africa is still lacking behind to comprehend culture and tradition to reduce the wide gender gap, thereby empowering females to enhance phylogeny?.Secondly, content and expression of this biological difference is exaggerated in the situation in SSA. Tracing back from history until date, most of the hardest and most commanding jobs are carried out by men therefore variety is constrain to exist b etween sexes. The fact that men are a stronger sex to resist extreme hash conditions makes them governing irrespective of sex division. Complex heathen societies are build up by institution that keeps men at a dominant dress. This make the female sex constantly relegated at the background. The norms and value that govern these complex societies (SSA) procure men at the forefront. By respecting this norms and determine women would be hardly seen in the public spheres. (Sushama Sahay, in king and Hill Anne. p 89)Thirdly, Inequality theory try to make some kind of biological differences that are sufficient and necessary to persistently cause contrariety between sexes and puts men at a commanding and dominant position. There are three imputed biological differences that have received much attention by the inequality view, such as reproduction by females, physical capacity and predisposition toward violence. Anthropologists largely agree that women have hardly occupied position of high status or political power than men in any rules of order anywhere, anytime (Buthler 2006) Some feminist theorist argue that, reproduction e verywhere is done by females that subordinates their position to men, others say that men are physically dominant in their actions and activities and set rules that are of their own advantage. Some theorist argue that men are very aggressive than women, that put them at a dominant position thereby creating inequality between the both sexes. (Buthler 2006 P 78)Lastly, apart from huge gender inequality and female empowerment sluggishness in SSA, inequality can too be traced amongst races and class. There have been and there are still traces of inequality amongst the tweed race and black race as well as amongst the upper and lower class group. There are two different kinds of historical inequality, mannikin that can illustrate this point. First of all, I entrust want to look back at the history of colonialism and neocolonialism in SSA b y the west that alone emit volumes of inequality and domination over a continent and makes a particular race dominant over the other. The history of racial inequality amongst the blacks and whites in the join States of America also illustrate an example of inequality amongst races.On the other hand, there have been inequalities within races and cultures. The upper and noble classes in SSA have been dominant over the lower and powerless group. This means that a superior culture is imposed and forced on to the weaker group that makes them not equal. Just like the history of European nobility over the commoners in Europe. Yet the nobility have remained a powerful and privileged class in most European nations. From biological and racial distinction on the inequality theory, inequality is a fact amongst genders, cultures, class and race, although times have changed and things must change, this pushes us to criticize the inequality theory with changing times.Critique of the Inequality T heoryThere have been a lot of theorists to critique speculative accounts on gender differences and female empowerment in SSA which creates inequality, besides very little progress have been made to prove one theory over the other in their speculative analyses on gender issues. To a large degree, inequality theories have not gained grounds because societies have distinctively evolved and disproved the speculative ideas of inequality theorist. In SSA straight past, traditional institutional arrangement have distinctively changed in respect of both genders not too much subordinating women like in the past. Looking at a typical traditional African purchase order, where farming is the scarce source of income, the man do the clearing of the farm while the woman do the planting and if harvest is good the subsistent crops are sold to maintain the family and educate their kids, both live in complementary way without gender distinction. Although traditional institution still exist and per sist today in SSA, but most if not all operate in the interest of both genders.Scholars argue that theories sometimes formulate persuasive speculative accounts which susceptibility fit what we already perceive or know. Therefore we must depend on the biological evidence provided by the inequality theory based on the reconstruction of inferences in well known societies to argue the inequality theory. Critics of Inequality theory also argue that, professional speculations of postmodern feminist by generalizing theories and with the political confusion by giving equal weight to every woman irrespective of race, class, sex orientation, culture and historical background makes origin of inequality theory to lost it attraction.The biological distinction of sex and gender roles as ascribed by the origin of the inequality theory is almost becoming baseless in SSA societies today. My argument is that inequality theory relies on female reproduction, the strength of men and the predisposition of men in violence situation as a prerequisite of being unequal. This was true to an extent tracing the origin of the theory, but today societies have evolved with changing times, no society in the history of mankind is static. Reproduction have just become a female experience and also a sex difference which has little impact on gender roles today. In the other hand, today in SSA men are only dominant in specific jobs as that they are specialized in, not that specific jobs are ascribed for men although the both sexes co-exist in a traditional way, but there is rational distribution of resources and effort so that girls and women can be empowered in this communities and families.The problems that arise sometimes are how to comprehend this inconsistent inequality that continues to persist with changing times. Theoretical efforts must be accepted to a certain degree and also the theory can only predict the future and to a larger extent crudely reconstruct the origin of inequality. The re is evident that the system of inequality like any other social institution is becoming self sustainable today in most SSA societies. Individuals are born sexed but not gendered they have to be taught to be masculine or feminine. One is not born but rather becomes a woman, it is civilization as a whole that produces this creature, which is described as feminine (Simone de Beauvoir 1952 p 267)The idea of inequality between men and women is created in the gender process interest the way cultural institutions are arranged. Therefore inequality in itself does not exist between sexes but created in the act or reaction in each society. Butler 1990, argues that gender as a process creates the social difference that defines man and woman in social interaction through their live, individuals learn what is expected, see what is expected, act and react in expected ways, thus simultaneously construct and maintain the gender order in each society (Butler 1990 p 145) In a typical African soci ety, though still primitive and traditional the inequality do not actually exist but it is the gender roles that differ from family to family and from community to community. Take for example within the Muslim religion or culture in SSA women are actually distinctive in their socially constructed roles ascribed by the religious laws. This does not mean that they do not live in a complementally as opposed by the inequality differences basing on sex division. I therefore argue that the issue of gender is a matter of hearing within families and communities, who should do what at a given time irrespective of the sex backed by norms and laws of that community. West and Zimmermann, hold waters that in humans there is no essential femaleness or maleness, femininity or masculinity, womanhood or manhood, but once gender is ascribed, the social order constructs and holds individuals to strongly gender norms and expectations. (West and Zimmaman 1989, P 146)The origin of the inequality theory have been attacked by it critics seriously in recent times. Recent studies also indicate that inequality would eventually lose it content as time evolves. The debate is centered on race and class subordination of inequality that existed in the past, but is currently loosing it value. It is clearly evident that racial inequality is gradually disappearing between and within races and class. I will like to illustrate this point on the colonial history of SSA. Africa have longed been colonized by Europeans to maintain a superior race and keep the African race subordinated under their control just like gender and sex. But because inequality is gradually loosing it originality in history, racial inequality have gradually dim away with changing times. Although some traces of racial inequality persistently exist between races. (Gramsci 1971, P 165)Another example that has made inequality lose it originality have been between whites and black Americans as well as European nobility. Whites and blacks have faced a long history of racial segregation in the join States, but because of time factor and new institutional arrangement the racial differences have almost disappeared. In the other hand, European nobility class use to be a more(prenominal) armed, politically and economically powerful class to the commoners in Europe but with the coming of decentralization of leadership and democracy this superior class have gradually disappeared thereby melting away the idea of inequality and subordination of commoners since everybody have an equal opportunity.Well as much as SSA is concern there have been inequality in class division irrespective of the gender differences. Inequality have been gradually disapproved since the old traditional institutions are disappearing and new wants sees everybody the same. In SSA, apart from gender inequality, there have been upper and lower class inequality as well as battalion from the royal fondoms, are always seen other than with high e steem. The upper class have been people who broadly enjoy high social amenities in the big cities of SSA, they have little or no gender differences between their families since almost everybody have a good education as compared to the rural unequal who cannot even provide for a daily meal. They are not much educated so definitely they believe in traditional laws that puts the men at the forefront. But with changing times and the fight for global poverty reduction, development in these topical anaesthetic areas in SSA is gradually improving making gender inequality to extensively disappear. On the other hand, Fondomites in SSA have maintain an extensively unequal powers in every aspect in SSA, this is because most traditional laws do respect and give special consideration to everyone from the fondom. But with the coming of democracy and the respect for human right and dignity, this traditional superiority is extensively disappearing there by making the class values to loss it weig ht. Today whether from the fondom or not, everybody is the same because of democracy. Though there have been a mixture of traditional laws to republican values to combat the aspect of inequality amongst fondomites and common citizen. (Foucault 1972, P 223)Importance of the Inequality Theory to the projectTo begin with, inequality theory is essential in this project because it explains the origin, history and persistent pre-domination and domination of males in almost all aspects of life in SSA. Through this theory, I regard that socialization, tradition and biology are interwoven to explain the persistent male domination in most SSA societies. To better actualise the importance of the theory to this project, I will like to raise each role played by each of these concepts to understand the role of inequality theory to the project.Men and women yesterday and today think and act differently and achieve differently in the varying regions in SSA (Banque and Waren 1990, P 90)Connectin g inequality theory to socialization, it helps me to distinguish between the upper and lower class socialization in SSA. To understand the importance of socialization in this project, it has to be treated differently with divergent identities and expectations. Socialization has helped me to understand why there is little or no gender inequality and more female empowerment in the urban than rural families in SSA. I have used socialization to compare inequality in urban and rural areas, which further makes me to understand class division in the two areas. It is certain that gender equality and female empowerment is higher in urban than rural milieus, because in the urban areas, generally, individuals and families are exposed to high social amenities and high standard of living. Social interaction is generally more modern than in the local interior in SSA. The upper wealthy class is found in urban areas while the lower miserable and primitive class is found in the local areas. Therefo re, as a result of this social division, inequality theory through socialization has helped me to distinguish and understand this phenomenon in details and further explains why there is persistent inequality in class and socialization in SSA.Connecting inequality theory through tradition, it has helped me to understand why there is still a wide gender gap and low female empowerment in typical traditional SSA societies today. People venerate traditional established ideas and teach them to their children. But what is the source of the gender traditions by which women are made everywhere subordinate. (Drage 2003, P 23) From the origin and history of inequality theory, men have established ideas and institutions that have always kept them dominant letting females at a subordinated position. The theory is therefore important in this project because it lets me understand why some primitive ideas are still led down from generation to generation in sub-Saharan Africa. Take for example, in most local communities in SSA, male inheritance have been a long established traditional belief and have been passed down to generations for centuries. These practices have become stronger so much so that even a male unborn child is celebrated before delivery. Women are regarded as properties and sold out for marriages, since bride price is been paid on them. Females have also been considered as products because they are forced into early marriages to reduce poverty since they are been bought by paying a bride price to their parents.Tradition is held at high esteem and has been a led down idea and still exists today in most of the local communities in SSA. By believing that only a male child can inherit property, has placed male sex dominant over females. This established idea have retarded development because resources are not rationally distributed by both sexes thereby making the female sex subordinated. As a result of this established believes, inequality persistently exists in this primitive areas that are reluctant to accept new changes because of illiteracy and poverty. Inequality theory is therefore important in this project because it has deepened my understanding of the free burning male domination because of these established ideas that have been passed down to generations. Inequality theory is also relevant because it explains these beliefs in such ideas and goes a long way to increment gender inequality and reduce female empowerment in SSA.Although there have been some changes in this traditional beliefs, but these changes mostly affects exposed families that is families that have acquired good education and have been exposed to more valuable cultures. Inheritance in these situations goes with responsibility and how you can manage the resources irrespective of being a male or female, though most often it ends up with problems from males since it has always been like that in most of the societies in SSA. Giving authority or property to a female i s just like depriving a male from his traditional right. But with continuous realization on how these have been affecting the societal development, I in person think it is going to disappear with changing time. Thanks to the inequality theory that I am able to explain this primitive belief in most of SSA families and societies.Connecting inequality theory through biology, it is relevant in this project because it has made me understand male domination in biological distinction of both sexes. This is because women and men are physically different in ways that make men to feel dominant. Through biological distinction in inequality, I came to understand why there is inequality in labor division. This is so because the theory persistently insist on the physical strength of men to occupy certain jobs. That is why there has been persistent gender discrimination in organizations and job opportunities because men think that some jobs can be physically carried out by them. For good example in SSA, it is hard to hear that a woman is a military general, bus driver, engineer, carpenter, technicians and or family head. Biological explanation also emphasize on the predisposition of men in extreme dangerous situation so to speak. In SSA men have always been involved in warfare and critical traditional decisions that involves sacrifices are carried out by men. Therefore, as a result of this, inequality is bound to exist and that is why I have employed it in my project to understand this in greater details.However, with the advent of feminist theorist, and changing time, biological arguments for inequality in gender is gradually fading away. Technological improvement have made most jobs to be operated by machines and intellectual based not physical fitness. Therefore, both males and females can be trained to manipulate these machines to have a gender balance in job markets. However, since traditional African societies are still very backward and have not yet attained some lev el of technology, most jobs are still based on physical strength to acquire them. That is why biological explanation of the origin of inequality in gender is still very visible in SSA. Inequality theory is therefore useful to this project to understand the biological explanation of persistent inequality in physical strength, predisposition of men in dangerous situations and the reproduction of females that have made them subjugated and subordinated position since the beginning of time immemorial.The modernization TheoryAccording to (Deutsch 1961 Rostow 1960 Ruttan 1959), modernization theory evolved from two ideas about social change developed in the nineteenth century the conception of traditional vs. modern societies, that viewed development as societal evolution in progressive levels of growth (Deutch 1961, Rostow 1960, Ruttan 1959) Following a modernization tradition, problems that have held back the development and empowerment of females in SSA have been irrational allocation of resources. Modernization theorist believe that for traditional African societies to become developed, there should be a rational distribution of resources for both sexes and the riddance of traditional, institutional and organizational roadblocks that have made Sub-Saharan African societies underdeveloped. Therefore, the society must pass through transformational corresponds to become modern.General Assumptions of the theoryFollowing Rostows modernization assumption, there have been five circular stages a society must pass through to become modern such as traditional society, presumption for take-off, take-off, the drive toward maturity and the age of high mass consumption (Rostow 1963, p 127)The stage of traditional society is characterized by primitive technology, pre-Newtonian science and spiritual behaviors in the material existence. There is traditional gender inequality and no idea of female empowerment since the society is too primitive and recognizes male superiority . The traditional parsimoniousness depends soly on primitive methods of farming and limited productivity. There is limited mobility in the traditional society and most agricultural lands are owned by men limiting the female powerless and have absolutely no say in land ownership. That is why development is still imbalance today in SSA because resources are irrationally distributed and there is no female inheritance of property. Since it is a linear simulate, for a society to move to a pre-takeoff stage it has to do away with some ideas in the traditional stage so that there should be a regular growth. (Peet and Hartwick 1999, P 81)The pre-take off society stage is characterized by development of modern technology and it application to agriculture and industry. Gender inequality is very high and there is little or no female empowerment because most machines were believed to be operated only by men. The idea of modernism was seen to develop sectors like educations, banking, commence, manufacturing and investment. This means that there was still very high gender discrimination in education and labor in SSA. handed-down African women could not own accounts according to traditional institutions and cannot be exposed to the public spheres. This was injected in a society that was still is primitive. (Ibid)The take-off stage as assumed by the modernization view as the stage for technological expansion, socio-political structures of society including gender rules in the distribution of labor in most urban areas in SSA. There is a little economic growth and a period to begin industrialization. In this stage, the word on gender and empowerment to modernize and enhance development increases in the urban and still very dormant in the rural sectors of SSA. (Ibid)The drive toward maturity stage is characterized by the spreading of technological expansion on economic activities and also there is sufficient entrepreneurship to practically fabricate heavy machines and equipm ent resulting from heavy industry. In this stage, the discourse on gender and participation have somehow gained grounds in most advanced societies and some prominent African cities. Women get more and more involved, the fight for economic growth and political dialogues and participation increases. (Ibid)The stage of mass consumption is characterized by the production of durable consumer goods and services. The rate of production of goods and services surpasses the ingest of consumption and employment is very high at the urban milieu in SSA. At this level there is little gender gap and female empowerment is high in most urban centers. This means that most families are exposed to western education and enjoy high standard of social amenities in the big cities. There is capability to invest in social welfare and social security on both genders, therefore cultural values comprehend modernity. (Ibid)Research have proven that most traditional African societies are at the take-off stage an d at this level of development gender inequality is still very high at the rural sector and the society is very reluctant to any social and developmental changes. This means that the society is still very traditional, primitive and reluctant to social and development changes due to strong traditional and cultural beliefs. Also the theory explains why development has not made any significant progress in SSA especially in the rural communities where there is still a very wide gap between gender and female empowerment in SSA.Modernization theory can be seen as the legacy of the ideas of progress developed in Europe in the eighteen century. This means that progress and evolution was viewed as an irreversible, natural and systematic path toward modernity. The idea of traditional vs. modern society propped up in the different stages of growth and development in each society. This evolutionary progress of society was seen as a transformational stage from the simple to the complex. Therefor e SSA being in the three stage according to the modernization vision, female empowerment and gender equality is very low, since the society is somehow very primitive andpre-occupied by male domination. Traditional beliefs which support female subordination is very high at this stage of development. (Latham 2000, p 37)According to Nick Cullather, the idea of natural pattern of progress and development, as assumed by the modernization theory is a set of ideas and discourse used as a strategy by US to try to single out the US from former colonizers in their actions toward terzetto world countries. (SSA). It was in the interest of the US as they also think that it was in the interest of the third world countries (SSA) to elevate third world countries to engage in the transformational steps toward modernity, this means that both sexes were to be involved in the stages of development thereby reducing the gender gap and empowering women in the process of development. The American idea c ould help assist third world countries avoid wasted steps in transition. This was seen as the Americanization and westernization of third world countries which was not more or less than the policy of assimilation by the French. (Black girls could eat and dress like French girls in French colonies to be assimilated and modern) (Nick Cullather, 1997 94)The modernization theory advocates two fundamental concepts universalism and linear process. Both concept had and have huge impact on gender and female empowerment in SSA. This means that girls and women in Sub-Saharan Africa have the same cultural and identical background to move from a traditional stage to a modern stage in universal and linear order of development. (Redfield quoted in Cullarther) Supported by the same vision, all societies in SSA were seen as taking the same pattern toward modernity through recognizable stages, without considering other historical background, origin and geographical conditions. In the same light, fol lowing a modernization vision, all cultures were seen in a escape way. Therefore the theory never considered cultural institution, tradition, and customs and viewed as obstacles to female empowerment and gender equality. (Cullarther). By classifying the society in a one pattern way of development, the theory was therefore criticized by other prominent development theories such as the dependency theory, power theory and the rise of feminist thinking in SSA.Critique of the theoryModernization theory has received criticism in recent years from political scientists and political economists since it neglected cultural, historic, and socio-structural factors in it synopsis (Chirot,1986 Black, 1991 Wallerstein, 1980) The modernization theory has witnessed a lot of critiques from varying development theories to scholars, researchers, institutions and other development practitioners. Most prominent development critique of the modernization theory hold that cultural values would still conti nue despite the shift from a traditional to a modern society. Therefore the argument is that despite the modern values of the modernization theory to transform traditional African societies to become modern by reducing the wide gender gap and encouraging female empowerment, African values still persist despite the values of modernity to enhance development in SSA. There is evidence that the broad cultural heritage of a society leaves imprints on values that endure despite the forces of modernization in other words cultural change depends on a societys cultural heritage. (Inglehart 2000c)Sub-Saharan Africa is made up of diverse cultural backgrounds, origin and history of migration. Though jointly colonized by the West, the fact that the society is culturally divided in origin and history, the values of modernization cannot hold at the same pace in the African societies respectively. This means that linear and universalism of the modernization theory could not work effectively in SSA and considering the fact that societies give different respects to their cultural heritage as considered by the modernization theory as an obstacle for development. Take for example the Islam religion, practices and beliefs is very strong in the Muslim society in SSA, therefore the issue of gender and empowerment of Muslim women can be a serious disorganization of religious rights since the later is very stiff in it traditional religious claims. The modernization theory had never interpreted traditional religious beliefs into consideration as ascertain by many of it critics.

Role of Organic Geochemistry in Petroleum

Role of Organic Geo chemistry in crudeA review on role of total geochemistry in vegetable anointcharacterization and applications of different basinsHarish Chandra JoshiAbstractPetroleum is a mixture dominantly of hydrocarbons with varying proportions of non-hydrocarbon constituents and traces of organomet every last(predicate)ic compounds. Generally Petroleum has an average composition of 85% carbon, 13% hydrogen, and 2% of sulphur, due north and oxygen. The aim of study is to find out the physicochemical and genetic property of petroleum. In this study biomarkers, age specific biomarker and reservoir geochemistry can be apply for the characterization, correlation and/ or reconstruction of the depositional surroundings as micro and macro fossils used by the geochemist.Keywords Biomarker, transmissible Characterisation, Kerogen, Geochemical Fossils.IntroductionThe name geochemistry was first used by the Swiss chemist, Christian Friedrich Schonbein in 1838. Petroleum geochemis try is the application of chemical principles to the study of the origin, migration, accumulation, and alteration of Petroleum ( vegetable embrocate and gas) and the use of this knowledge in exploring and recovering Petroleum. Organic chemistry is the branch of chemistry that deals with the dissemination and composition of carbon compounds. Geochemistry is the study of the chemical composition of the earth, minerals, ores, flutters and also is the study of the origin of petroleum. The major tasks of geochemistry can be summarized as followsThe study of the relative and absolute abundances of the elements and of the atomic species (isotopes) in the earth.The study of the distribution and migration of individual elements in the various deducts of the earth (the hydrosphere, atmosphere and lithosphere etc.), and in mineral and flaps, with the object of discovering their distribution and migration.Exploration companies have used petroleum geochemistry in hydrocarbon exploration. Th e joltingly and major objective of exploration geochemistry, is to reduce the risk of drilling dry holes. Petroleum geochemistry is based on the radical origin of the oil and gas whereby innate matter obtained from dead plants and animals. Organic matter is converted to hydrocarbons in the subsurface through various major three stages of transformations diagenesis, catagenesis and metagenesis. German scientist Treibs (1936) reveal a relationship between chlorophyll-a in living photosynthetic organisms and porphyrins in Crudes of petroleum. This connect provides a strong evidence of organic originof Petroleum. From the starting of the Precambrian till the Devonian, the unique primary producer of the organic matter were marine phytoplanktons. Since the Devonian an increasing mensuration of primary production has been contributed by higher terrestrial plants. At present cenario marine phytoplankton and higher terrestrial ar estimated to produce about equal amounts of organic ca rbon. On increases the burial depth, porosity and permeability decrease, and temperature increases. Thus lead to the change a gradual halting of microbial activity and thus eventually called organic diagenesis to a halt. As the temperature rises, thermal reactions become increasingly. This second transformation phase, called catagenesis, during the catagenesis kerogen begins to decompose into smaller, more mobile grains. In the early stage of catagenesis, kerogens argon still comparatively large these ar precursors for petroleum and ar called bitumen. In the late stages and final transformation stage, called metagenesis. During metagenesis the principal products consist of smaller gas molecules. Further, kerogens create from different organic matter, or under different diagenetic conditions, argon chemically clear which has a significant effect on hydrocarbon generation.Characterization of crude oil by Analytical MethodsFirstly sampling of crude oils is required for their chara cterization. Oil should be collected as a single- phase sample under squash conditions as they argon in reservoir. Therefore for the geochemical studies, crude oil samples are collected at the well head under atmospheric pressure. Under these conditions unmortgaged hydrocarbons of crude oils are lost completely or partly. Light hydrocarbon fraction gives the ideas only about the abundance and constituents of the light end of the oil. It is normally observed that the most abundant feature films hydrocarbons are commonly in the light fraction. For required minimizing the effects of sampling error the crude oil is distilled at 2100C. The heavier fraction is considered the foremost part of the crude oil. It is used to describe the chemical composition of a crude oil and also to compare it with other crude oils.Analytical Techniques in Petroleum ExplorationPetroleum system (Demaison, 1994 Hunt, 1996) comprise all those geological elements and processes that are necessary for an oil an d gas deposit to occur in nature. These main elements are a petroleum writer rock, migration paths, reservoir rocks, seals, traps and the geological approach that design each of them. Such systems involve a genetic relationship between the source rock and the petroleum accumulations, but proof of that relation force a geochemical correlation. organic geochemistry techniques available include surface geochemical prospecting, source rock geochemistry, crude oil geochemistry, natural gas geochemistry, biomarker geochemistry, isotope geochemistry etc.Biomarkers in PetroleumBiological marker or shortened to Biomarkers (Seifert and Moldowan, 1981) are complex molecules derived from once living organisms they are establish in sediments and oil and show little change in structure from their parent molecules (Peters Moldowan, 1993 and Hunt, 1996). These compounds are also called as geochemical fossils (Eglinton and Cavin, 1967) because of their origin from living organisms. Such compounds may be derived from terrestrial (mostly plants, marine pelagic (mostly plankton) and marine benthonic (algae, bacteria and other microbes). Biomarkers are generally, microfossils less than 30 nm in diameter and are highly variable in their stereochemistry i.e. the spatial arrangement of atoms and groups in their molecules.The common use of the biomarkers in petroleum exploration may be enumerated as followsBiomarkers are present in both and oil a source rocks so they provide vital information for the oil-oil and oil-source correlation.Organic matter type (source of organic facies)Depositional environmentExtent of thermal maturationDegree of biodegradationInformation about the age of the source rock and Geometry of BiomarkersSteranes obtain from the diagenesis of natural products sterols. Diagenesis converts sterol via chemical dehydration and microbial reduction to a steranes cholestane. Cholestane molecule is drawn in three dimensions as follows. The hydrogen at the 3 position p oints up above the plane of the molecule and that at the 5 position points down below the plane (Peters and Moldowan 1993) habitually Used Biomarkers in Petroleum ExplorationNormal Alkanes Normal alkanes are a homologues series of saturated hydrocarbons of general formula CnH2n+2. All linear n-alkanes from C1 to C40 and a few beyond C40 derived from different sources have been identified in crude oils.Iso- and Anteiso-alkanes Isoalkanes are 2- methyl radical alkanes and quite a number of these have been observed in crude oils as have been the anteiso-alkanes, the 3-methlyalkanes. Iso and anteiso alkanes are associated with n-alkanes in plant waxes where they comprise a approximate number of carbon atoms (about 25-31) with an odd predominanceFigure 1. Showing common biomarkers like paraffins, Iso and ante-isoalkaneAcyclic Isoprenoid These are special type of Iso-alkanes in which one methyl group is attached to every fourth carbon atom in straight. Isoprene (methyl butadiene) is the b asic structural unit composed of carbon atoms that is found in all biomarkers. The most common isoprenoids are pristane (C19) and Phytane (C20).Figure 2. Common Isoprenoid biomarkers in petroleumTerpenoids Terpenoids can be classified based on structural types into diterpenoids and triterpenoids Diterpenoids are categorized into bicyclic and tricyclic diterpenoids. Triterpenoids are grouped into tetra and pentacyclic. The most knowing are pentacyclic and among these are hopanes. Hopanes are pentacyclic triterpenoids comprised of four 6-membered and one 5-membered ring. There is a side chain which can contain upto 8 carbon atoms. Thus the series comprise of C27-C35 hopanes. They are believed to have originated from polyhydroxybacteriohopane.Figure 3. Structures of Common TriterpanesFigure 4. Structures of Common Tricyclic and Tetracyclic TerpanesSteranes Steroids can be classified as aliphatic and aromatic steroids (mono, di- and tri-aromatic depending on the number of aromatic rings ). Steranes are a series of aliphatic steroids. The sterols in all eukaryotic organisms are precursors to the steranes in sediments and petroleum. Like the hopanes, steranes are abundant in sediments, rocks and petroleum, because their precursors (Sterols) are so common in living organisms. Cholesterol has octonary asymmetric centers and might be expected to show as many as 28 or 256 stereoisomers.Figure 5. Chemical Structure of various steroidsPorphyrins Porphyrins are characterized by a tetrapyrrolic nucleus proved to be inherited from chlorophyll, the green photosynthetic pigment of plants and animals ,hemin, the red pigment of animal blood. These tetrapyrrolic organometallic compounds reported of the vanadium and nickel in petroleum. The major types of fossil porphyrin are deoxophylloerytrapyrrole (DPEP) and etioporphyrin (ETIO) porphyrin structure.Age specific biomarkersIf biomarkers characterise a molecular record of life, they can be used for age determination. reliable ag e specific biomarkers like Oleanane present in oils derived from late Cretaceous or Younger. C11-C19 Paraffins, Odd carbon number prevalence in oil from many Ordovician sources. 24-n-propylcholestane, High in oils from Ordovician sources.Thus the biomarkers transport to the sources has proved to be of great help in geochemical characterization of the oils/condensates.Reservoir GeochemistryThe main aim of reservoir geochemistry is to understand the distribution and origin of the petroleum, water and minerals in the reservoir and account for their possible spatial and compositional variation (Cubitt and England 1995). A better understanding of the fluids in the reservoir make out to a better understanding in an area and prioritization of exploration thrusts. The principle factors responsible for difference in petroleum composition are the effect of organic facies variations, progressive source rock maturation, migration fractionation, gravity segregation, oil/water contact and non-un iform biodegradation of oil across the field. However these effects have been normalized by using ratios of peaks agree to compounds of similar molecular weight in the C10+ region of the chromatogram.The study of reservoir continuity is also the focus of the geochemical characterization to trace the nature and depositional conditions of the source organics, realisation of the oil families and thermal maturity of the oils/condensates.When a set of chromatographic peaks has been selected, a variety of techniques are available for grouping of this data. One way is to use a diametral plot of selected ratios by a star diagram (polygon plot) by plotting each peak ratio on a different axis of polar plot. Each data point is plotted from the centre of the concentric circles outward. The points are then connected to create a star shaped pattern characteristic of each oil.Applications of geochemical characterisationBiomarker and non-biomarker geochemical parameters are best used together to supply the most authentic geological interpretations to help solve exploration, enlargement, production and environmental problems. prior(prenominal) to biomarker work, oil and rock samples are properly screened using non biomarker analyses. The strength of biomarker parameters is that they provide more detailed information needed to answer questions about the source rock depositional environment, thermal maturity and the biodegradation of oils than non-biomarker analyses alone. Different depositional environments are characterized by different assemblages of organisms and biomarkers. Commonly accept classes of organisms include bacteria, algae, and higher plants. Biomarker parameters are also an effective means to take in the relative maturity of petroleum through the entire oil-generative window.ConclusionOn the basis of above observation major conclusions which have been derived from the whole study are as followsThe presence of complete range of normal alkanes upto nC36 and i n some cases upto nC40. The presence of biomarker in oil indicates that oil may be terrestrial or marine. The terrestrial nature of the source is also strongly indicated by the steranes. Reservoir geochemistry of oils has been used to demonstrate the lateral/vertical continuity/compartmentalization.ReferencesBhandari, A., Prasad, I.V.S.V., Kapoor, P.N., Varshney, Meenu, Madhavan, A.K.S., Pahari, S. and Singh, R.R., 2008. Depositional environment, distribution of source rocks and geochemistry of oil and gases, Krishna-Godavari Basin, Journal of apply Geochem., Vol. 10 (1) pp 17-31Bhandari, A., Prasad, I.V.S.V., and Dwivedi, Prabhakar, 2007. Stratigraphic distribution of hydrocarbons in the Sedimentary Basins of India. Symposium in Applied Geochemistry in the evaluation and management of onshore and offshore Geo sources. Journal of Applied Geochemistry, Vol. 9 (1) pp 48-73.Bhatnagar, A.K., Goswami, B.G., Rawat, G.S., Singh, Harvir and Singh, R.R., 2009. Geochemical characterization a nd reservoir fingerprinting to assess reservoir continuity in oils of Heera and South Heera fields, western offshore basin, India, Petrotech 2009 naked as a jaybird Delhi.Cubitt, J.M., England, W.A., 1995. The Geochemistry of Reservoirs. The Geological Society London, pp 321.Demaison, G.J and Huizinga, B.J., 1994. Genetic classification of petroleum systems using three factors charge, migration and entrapment. In The Petroleum system From source to trap (L.B. Morgan and W.G. Dow, eds), American Association of Petroleum Geologists, Tulsa, pp. 73-89.Didyk, B.M., Simoneit, B.R.T.,Brassel, S.C and Eglinton, C., 1978. Organic Geochemical indicators of pale environmental conditions of sedimentation. Nature 272, pp 216-222.Eglinton, G and Calvin, M., 1967. Chemical fossils. Scl. Am. 216, pp 32-43Hunt, J.M., 1979. Petroleum Geochemistry and Geology. W.H. Freeman, San Francisco, pp 617.Hunt, J.M., 1996. Petroleum Geochemistry and Geology. W.H. Freeman and Company, tender York.Pandey, I.P. , Joshi, H.C., Tyagi, Ashish Tiwari, Sadhana and Garg, Nitika, 2012. Study of the Parameters and Bio-Markers of Crude oils. Advances in Pure and Applied Chemistry, World Science Publisher, New York, United States, Vol. 1, No. 3, pp 49-53.Mackenzie, A.S., 1984. Application of biological markers in Petroleum Geochemistry, In Advances in Petroleum Geochemistry, Vol. 1, (J. Brooks and D.H. Welte, eds) Academic Press, London, pp 115-214.Mackenzie, A.S., Patience, R.L., Maxwell, J.R., Vandenbroucke, M and Durand B., 1980. molecular parameters of maturation in the Toarcian shales, capital of France Basin, France-1. Change in the configuration of acyclic isoprenoid alkanes, steranes, and terpanes. Geochimicaetcosmochimica Acta, 44, 1709- 1721.Peters, K.E., 1997. Modern Geochemical Tools for efficient exploration and Development, O.G.C.I. Training report, Oct. 20924, Mussoorie, India.Peters, K.E. and Fowler, M.G., 2002. Application of Petroleum Geochemistry to Exploration and reservoir mana gement. Org. Geochem. Vol 33, pp 5-36.Peters, K.E. and Moldowan, J.M., 1993. The biomarker guide interpreting Molecular fossils in petroleum and ancient sediments, Prantice Hall, Englewood Cliffs, NJ., U.S.A.Seifert, W.K. and Moldowan, J.M., 1978. Application of steranes, terpanes and Monoaromatics to the maturation, migration and source of oil. Geochem. Cosmochim., Acta 42, pp 77-95Seifert, W.K. and Moldown, J.M., 1979. The effect of biodegradation on steranes and Terpanes in crude oil. Geochem. Cosmochim., Acta 43, pp 111-126.Seifert, W.K. and Moldown, J.M., 1980. The effect of thermal stress on source rock quality as Measured by hopane stereochemistry.Physics and chemistry of the earth, 12, pp 229-237.Smith,H.M., 1940. Correlation index to aid in interpretin crude oil analysis. U.S. Bureau of Mines, tech. Paper610.Tissot, B.P. and welte, D.H., 1978. Pertoleum formation and Occurrence, Springer- Verlag, New York, pp. 699.Tissot, B.P and welte, D.H., 1978. Pertoleum formation and O ccurrence, Springer- Verlag,Berlin.22.Treibs, A., 1963. Chlorophyll and hemin derivatives in organic mineral substances. Angewandte Chemie, 49, pp 682-686.1

Burnout, contributing factor and how it relates to job performance Essa

In the recent years, organizations have paid extra attention to employee stress and its effect on personal line of credit performance. Burnout, an outcome of stress is known to cause individual, family and organizational problems and health conditions such as insomnia and hypertension. The question many another(prenominal) another(prenominal) ask is where does it originate from? And, how supported are the employees by the organization? Researchers have attempted to link stress and burnout and its effect on job performance. This research analysis includes different scholarly studies done and that found many add factors such as job satisfaction, work and family demands, work environment, and culture. Ivancevich, Konopaske, & Matteson, 2011 defines burnout as a psychological process, brought about by unrelieved work stress that results in emotional exhaustion, depersonalization, and feeling of decreased accomplishment. Examples of emotional exhaustion includes feeling drained by w ork, fatigue in the morning, frustrated, and do not want to work with others. depersonalization neurosis is when a person has become emotionally hardened by their job, treat others like objects, do not care what happens to them, and feel others blame them. A pitiful feeling of accomplishment also results from burnout. A person is unable to deal with problems effectively, identify or understand others problems, and no longer feel excited by their job. (Ivancevich et al., 2011). Researchers have united burnout as a contributing factor health conditions such as sleep disturbances, decreased immune system. Professions that are prone to burnout are those who require a great deal of contact and responsibility of other people. Among those professions are teachers, nurses, physicians, social workers, therapists, police, an... ...the country. (Hamwi, et al., 2010). In conclusion, the above research analysis explained many contributing factors to stress and burnout and its effect on per formance. As expected from prior studies, job satisfaction has an effect on productivity and/or burnout. Burnout in US nurses has been linked to Philippine nurses, despite a difference in health-care systems. Gender has also proven to be a contributing factor to stress. Women have a operative level of stress compared to men due to additional work of housework and childcare. Women also are linked to low levels of emotional exhaustion with co-worker support. Finally, intuition organizational support has been linked to emotional exhaustion, but not solely due to the organization. Hopefully, organizations will continue to adopt stress reducing programs and recognize that it has many contributing factors.

Essay --

Throughout my public life Ive been involved in mingled groups and mentoring circles. The question that comes up most is whats my next career move or how do I know Im successful? in that respect isnt a right answer to this question it depends on whats most important to you and your family. Do you need to be around for every association football game or own three homes? Are you looking to retire with as much money in the bank as possible or want to be home every night while working your way up the ladder? A career in atmosphere requires trade-offs.We striket know how to measure the success Airline Deregulation Act of 1978. Designed to remove government entry barriers, reduce price manipulation, and open up routes to incentivize competition between airlines was it triumphant? Its true the cost of an airline ticket is much cheaper now, but with an increase in mergers and acquisitions over the past x geezerhood, we once again have a handful of majors and regionals dominating the industry. How do I know what influences the industry and my steps as a career aviator? Its scary to think that many of todays airlines did not exist before deregulation.Why should a pilot care close to deregulation and the financial amplitude of the airline industry? Like it or not, tactical career moves could irritate the difference between declaring bankruptcy five years into your flying career or upgrading to captain to meet application minimums at the company of your dreams. Many people abandon a career in aviation the airline industry is cyclical and unpredictable.Im not an expert Im merely a professional pilot trying to make the right choice about which basket to place all my eggs. Im now preparing to make my next (and hopefully final) move. Will I set to make a ... ...s aircraft exhaust emissions. Where will I set my sights long-term, once my dues paying at the regional is behind me? The average annual flight trading operations wages based on 2012 DOT Form 41 ranges from about $88,000 (Allegiant) to $159,000 (Delta).Each airline has its own culture youve got to consider. Some are more captain-is-god in that respect are dozens of airlines in the US. From legacy to cargo, locals and regionals. Lets say youre a newly minted ATP. about 6 main regional airlines operate in the US. If you intend to fly for a major someday, you rattling need to get a Bachelors degree. Without it most of the carriers are tossing your application in the trash.There really is no fair comparison of airlines. Its all dependent upon your personal needs and wants.. FOOTNOTES 1.http//web.mit.edu/airlines/analysis/analysis_airline_industry.html