Critically evaluate the claim that Marxism offers a coherent account Essay

Critically evaluate the claim that Marxism offers a coherent account of the modern international political system - Essay Example The most notable example of its failure to build up a fruitful political system is the Soviet Union whose eventual collapse was engendered by the flaws and paradoxes lying at its heart. In the first place, Marxism fails to perceive the corruption of absolute power. Secondly, it fails to perceive that ruling proletariats in the power of a socialist state are bound to assume the behaviors of capitalist elite class. The third flaw lies in its attempt to impose equality and to steal away freedom in the name of a classless society. Such socialist attempt to maintain equality by robbing a man’s freedom is essentially Totalitarianism which inspires corruption and discourages people’s protest against this corruption. Further the paradox of an equal society does not permit a congenially productive economic system. When the equal distribution of wealth among the citizens theoretically seems to contribute to an equal and just society, this distribution ultimately discourages the i ndividual to involve in production activities. Thus a socialist economy goes futile. Again, a Marxist state continually aims at establishing the working class at the power of a country. Therefore, a socialist political system poses threat to other states which do not hold a socialist view. A Brief Overview of Marxism and a Marxist State Being commissioned by the Communist League, Karl Marx and Frederick Engels jointly wrote the book, â€Å"The Communist Manifesto†, in 1848, which is often accepted as one of the most influential political documents around the world. Indeed, Marxism is regarded as a social theory which foretells and philosophizes about the future of modern capitalist society as well as human history. But it is also true that the Communist Manifesto can be considered as the political guidance for those who are involved in the production system of the modern capitalist society. For these authors, modern society is essentially the latest one of those changes in th e mode of production throughout the evolution of human society. At the same time, since Marx and Engels envisaged that the stage of human society, next to Capitalism, is a world of the working class people, in Marx and Engel’s word the proletariats, that is based on the socialist modes of labor as well as production, the proletariat or the working class people of the world should unite themselves to take the society to this stage. Theory of Class-struggle and Marx’s View of a Socialist Political System Marx and Engels have assumed that the Capitalist society has evolved from the breakdown of the previous feudalist society through the conflicts between the feudal landowners and their subjects. Through this dissolution of the feudal society two more incompatible classes emerged: the bourgeoisie and the working class. While the bourgeoisies, occupying a country’s political system, control the instruments of production, the working-class are economically subjugated by those in a capitalist political system. These bourgeoisies are exclusively profit-driven. Though they lack any morals, they continue to support any moral system which is congenial to

Lord of the Flies Central Thematic Dichotomy Essay Example for Free

Lord of the Flies Central Thematic Dichotomy Essay The theme of the central thematic dichotomy in lord of the flies is conveyed through many ways through out the first three chapters. The once majestic island has begun to seem as if it is only a mask for the true concealed â€Å"beastie.† The seemingly paradisiacal island is very similar to the Garden of Eden as it obtains beauty by the vast amounts of growing foliage such as the â€Å"blue flowers,† â€Å"candlebuds,† and dense green forest. On the crust the cool, calm and peaceful persona is vivid and clear however just below lies the greed and savagery of all man kind which in the biblical story says that evil was exposed through Eve when she gave into temptation and disobeyed god’s commands however, in Lord of the flies the sinister one who gives into temptations and leads others with him is Jack as he gives into his primal urges and disobeys his civilized upbringing because of his constant rationalizations of the fact that they need meat but, in reality his interest in meat for the boys is clouded by his desire to kill. The garden of Eden references are also foreshadowing devices as at first the Garden is full of joy and laughter and then humans fall because of greed and temptation which is what happens to the boys at first they believe â€Å"this is a good island† but soon they fall to the primal instincts from within. This shows that the island although beautiful on the surface is merely a disguise for the underlying evil that is rooting itself into the boys. Secondly in my opinion I believe the island and the boys is very much like Libya, it has a hierarchy, it has an infrastructure, it has rules and regulations. It seems civilized. However it is not. As we know, the hierarchy is a dictatorship conducted my Gaussian, much like Jack who is urging himself to be a dictator which is shown when he says â€Å"Come on, Follow me!† leaving only Piggy and Raplh alone as well as, his control over the choir boys, even though Ralph is leader. Its infrastructure is weak, as is the shelters built by the boys, and lastly the rules and regulations are kept but enforced in a cruel , barbaric way which is how the boys are beginning to edge towards as shown when Ralph makes the boys stand until one collapses and he is left and teased for his lack of stamina. Although the boys try to stay within the paths of civilization they slowly are drifting onto the trail of savagery. Also the components of civilization they brought or found are slowly being destroyed and replaced with demonist items such as fire. They begin with their clothes. Clothes have been worn for 170,000 years, since the dawn of civilization, at first the purpose of piggy removing his â€Å"school sweater† is because of his desperation to escape the heat and although Raplh strips, during the first chapter he eventually puts his clothes back on, showing that within him he still sides with humanity rather than savagery however, as time continues the boys slowly begin to rip and destroy as well as remove more clothing and the most covered, Jack, who begins wearing his long black cloak in the first chapter becomes the least clothed with his â€Å"bare back† described when he is hunting in the third chapter. This shows the backwards evolution of the boys where they are beginning to strip away all that is civil and proper and go back to the nature they were created with. As if the once civilized island is now manipulating them to become Neanderthal like creatures. Secondly Piggy’s glasses represent intelligence and humans overpowering nature and the boys use them as †burning glasses† which shows that they are starting to lack respect for human kinds inventions and innovations and interchange over to a side where items/technology are not worth what they would be in a civilized town. Also the fact that they took the glasses forcefully without Piggy’s consent is a sign that the children have not only begun to loose respect for items but also for manners which coincides with the theme that the boys are loosing respect for proper behavior and therefore loosing respect for civilization. This again also foreshadows the future because Piggy’s glasses are also broken just like the shattering of civilization on the island. Lastly the fire the boys create begins as a sign of hope, they try to create a signal so someone might rescue them however it slowly turns into a much larger fire than expected and sets fire to some of the trees and eventually kills a young boy. This is Goldings way of saying that even something that has good intentions can quickly turn into something heinous if given the chance. It is foreshadowing the boys development from good natured English school boys to savage, cold, cantankerous monsters of human beings. To conclude on the surface the boys and the island seem, pleasant, empyrean and majestic however, the inner core opposes the outer drastically and surfaces when times are tough. This shows that the primitive barbaric attitudes of our ancestors comes fourth when in a time of crisis and pressure even if we have the greatest of intentions in the end as Golding shows no-one has the ability to deny or defeat our urges even ones as pure as Simon. Our greatest enemy is truly ourselves.

Data Stream Classification Of Red And White Wines Marketing Essay

Data Stream Classification Of Red And White Wines Marketing Essay Introduction Well we got 2 data sets to analysis using SPSS PASW 1) Wine Quality Data Set and 2) The Poker Hand Data Set. We can do this using CRISP methodology. Let us look what is CRISP by wikipedia CRISP-DM stands for Cross Industry Standard Process for Data Mining It is a data mining process model that describes commonly used approaches that expert data miners use to tackle problems. PASW Modeler is a data mining workbench that enables you to quickly develop predictive models using business expertise and deploy them into business operations to improve decision making. Designed around the industry-standard CRISP-DM model, IBM SPSS PASW Modeler supports the entire data mining process, from data to better business results. CRISP DM, Clementines own lightweight methodology of 5 stages Business Understanding, Data Understanding, Data Preparation Modelling, Evaluation and Deployment. CRISP Methodology Business Understanding: Understanding the project requirements objectives from a business perspective, and then converting this knowledge into a data mining problem definition Data understanding In this step following activities are going on, Data understanding, Collecting Initial Data then describing Data, Exploring Data and lastly verifying Data Quality The data preparation phase Tasks include table, record, and attribute selection as well as transformation and cleaning of data for modeling tools.Cleaning Data using appropriate cleaning and cleansing strategies then Integrating Data into a single point. Modeling: Selection and application of various modeling techniques done in this phase, and their parameters are adjusted to optimal values. Basically, there are more than one technique for the same data mining problem type. Some techniques have specific requirements on the form of data. Therefore, stepping back to the data preparation phase is often needed. Steps consist of Generating a Test Design, Building the Models assessing the Model Evaluation Building of model (or models) takes place in this phase. Before proceeding to final deployment of the model, it is important to more thoroughly evaluate the model, and review the steps executed to construct the model. Deployment In the final stage Knowledge gained is organized presented so that an end user can easily use it. As per the requirements this can be a report or a complex data mining process. Normally Customers carry out the deployment step Wine quality data set Wine quality is modeled under classification and regression approaches, which preserves the order of the grades. Explanatory knowledge is given in terms of a sensitivity analysis, which measures the response changes when a given input variable is varied through its domain The red wine data set contains 1600 samples out of which I have selected 200 random samples and doing the analysis(Data mining cannot discover patterns that may be present in the larger body of data if those patterns are not present in the sample being mined ) .So I selected the data set bearing in mind. The data set I have selected has high confidence. With measurements of 13 chemical constituents (e.g. alcohol, Mg) and the goal is to find the quality of red and white wine. Input variables 1 fixed acidity 2 volatile acidity 3 citric acid 4 residual sugar 5 chlorides 6 free sulphur dioxide 7 total sulfur dioxide 8 density 9 pH 10 sulphates 11 alcohol Output variable is quality (score between 0 and 10) CRISP methodology has been followed through out the phase .By checking the web site and resources learned about the wine domain .the next step was to check whether incorrect, missing or abnormal values in the data set end ensure the data quality. Data quality of the data set is very good. PASW Data stream classification of red and white wines Classification for Red and White wine 2 data sets red wine and white wine have been imported using variable file nodes Use of type node here is to describe the characteristics of data. . The Classification and Regression (CR) Tree node is a tree-based classification and prediction method. Similar to C5.0, this method uses recursive partitioning to split the training records into segments with similar output field values. The CR Tree node starts by examining the input fields to find the best split, measured by the reduction in an impurity index that results from the split. The split defines two subgroups, each of which is subsequently split into two more subgroups, and so on, until one of the stopping criteria is triggered. All splits are binary (only two subgroups) Red Wines variable importance White wine variable importance From variable importance diagram we can say that important attribute to determine Red wine quality is pH. The variable importance is in the order pH, citric acid, chloride as shown in the figure1. But for determining White wines quality the most contributing attribute is chloride and 2nd attribute is Alcohol. Decision Tree Model of white wine (only a portion) Analysis and conclusion The above generated tree consists of nodes and its children. The top node represent the total number of wine samples and how many number belongs to different categories(1 to 9).The first split is on chloride. This implies that most of the wine belongs to chloride level0.041.We see that good quality wine has chloride level It has been found from count Vs Quality graph that how many belongs to good quality categories. Alcoholic concentration of white wine samples is more than that of red wine sample. Good wines normally have high concentration. So we can conclude that White wine samples are good. In the white wine chloride level is normally high that implies it has got good Aroma. Where as in red wine the citric level is between particular levels that shows the red wine is very tasty!! PASW has got a number of 2-D and 3-D charts like bar, pie, histogram, scatter etc for time being I am using linear graph and 3-d scatter graph. You can use any of the graph as per the requirements. Some graphs are easy to interpret .Let us consider a 2-D graph between most contributing variable pH and quality from the graph it is clear that the relation ship between pH and quality is in such a way that if pH is in between 3.23 and 3.27 quality is good. Quality is very low for 3.38 and 3.50.We can plot similar graph between quality and citric acid or towards what ever contributing variable then find out the relation ship between them 2-D graph represent the relation ship between quality and pH of Red wine Let us plot a graph between chloride and Quality for the white wine. In the below figure it shows the quality is very good when chloride level below 0.036.And quality in the range 5 to 6 when chloride level is above .048. Like this if plot a graph between quality and alcohol we will see the quality is too good if alcoholic concentration in between 12.5 and 13(as per the sample I have analyzed) 3D graph which shows the relation ship between alcohol, quality and chloride level of white wine from the 2d analysis it was shown how the quality is being affected by single variable. If the one variable does not tell about how quality being related we can check relation ship between 3 variables using a 3d graph. It is having 3 axes. How Regression is useful In this multiple regression ,Predictors such as (Constant), alcohol, fixed acidity, residual sugar, chlorides, volatile acidity, free sulfur dioxide, sulphates, pH, total sulfur dioxide, citric acid, density determine the value of quality. Below gave a Pasw stream for regression. As per the variable importance graph volatile acidity, total SO2 and alcohol are most important variables in Regression analysis. Model R R Square Adjusted R Square Std. Error of the Estimate 1 .792(a) .626 .474 .542 Each by changing the independent variables value we can get value of dependent variable quality. With the help of a hypothesis we need to understand and build a relation ship among the variables. To predict the mean quality value for a given independent variable (say volatile acidity) we need a line which passes between the mean value of both quality and volatile acidity and which minimize the sum of distance between each of the points and predictive line. This fits into a line. The Poker Hand Data Set Each record is an example of a hand consisting of five playing cards drawn from a standard deck of 52. Each card is described using two attributes (suit and rank), for a total of 10 predictive attributes. There is one Class attribute that describes the Poker Hand. The order of cards is important and there are 480 possible Royal Flush hands. Below discussing about how to determine poker hands using data mining. I am considering classification only. If we consider clustering/Regression it does not make any sense PASW MODEL CLASSIFICATION USING CRT ALGORITHAM We got training and testing data set .First applying a model on training data set. Source file is a Comma separated file (CSV) with 1 million rows. It is difficult to do analyse on this input data set so selected sample data set and doing the analysis. Problem faced The given source data was not in a meaning full format so I have given meaningful attribute name and Values by using Vlookup function in MS excel, now the data has become more meaning full and it looks like below. Data cleansing is very important and comes under data preparation phase of the methodology Accuracy of predictive model The accuracy of predictive model is checked by analysis node. It has been found that accuracy is 90%. Using the Algorithm need to predict any of these: 0: Nothing in hand; 1: One pair;2: Two pairs;3: Three of a kind;4: Straight;5: Flush; 6: Full house;7: Four of a kind;8: Straight flush;9: Royal flush; Pocker hands variable importance diagram Let me say what did I understood from the diagram. Rank2 (rank of card2) is most contributing variable to predict poker hands. It is clear that Rank of 1st, 4th and 2nd cards are more contributing than suit of those cards. The different section of pie chart represents number of cards in a particular poker category. Blue represents No Poker; Red represents ONE PAIR, Green represent Royal flesh How Pasw helps to do classification Pasw has got number tree constructing algorithms(CR, c5.0) to do classification. I considered Classification and Regression (CR) though this is not a time efficient algorithm time complexity is more when compared to c5.0)I selected CR.The data set I have got is simple one and I am not considering the deep analysis all I need to do is to predict poker hands so CR can do it. Below shows the constructed tree using CR (Ashort description of tree already given above) Analysis Data has been classified into Training set and Testing set .Here most of the data set into a training set and small portion of data is used for testing.After a model has been processed by using the Training set, we can test the model by making predictions against the Test set. Since the data in the training set already contains known values for the attribute that you want to predict. Below giving the portion of training set being used. Integrating classification and association rule mining can produce more efficient and accurate classifiers.Here each row is an instance Trial: pair of 5 attributes (SUIT and RANK) + classification class. So this can be used to predict the classification of other unclassified instances. consider the training set given below suit1 rank1 suit2 rank2 suit3 rank3 suit4 rank4 suit5 Rank5 poker Heart ASS heart KING spades 4 Spades 3 heart QUEEN Nothing in hand Diamonds QUEEN diamonds 2 diamonds JACK Clubs 5 spades 5 ONE PAIR Hearts 10 hearts jack hearts king Hearts queen hearts 1 royal flesh Spades Jack spades king spades 10 Spades queen spades 1 royal flesh Diamond queen diamond jack diamond king diamond 10 diamond 1 royal flesh Hearts 5 diamond king spades king spades 7 clubs 5 two pairs Hearts 4 hearts 1 hearts 3 diamond 5 diamond 2 straight Suppose want to predict below hand is what type of Poker hand? suit1 rank1 suit2 rank2 suit3 rank3 suit4 rank4 suit5 rank5 poker Club 10 club jack club A club king club queen From the training set data the testing set is predicted, answer is Royal Flesh Conclusion Two data sets the wine and poker have been analysed using CRISP methodology and using the tool IBM SPSS PASW, used different modelling techniques which suits. Analysed the knowledge elicited by each model DATA MINING KNOWLEDGE DISCOVERY IN MARKETING (PART 2) Abstract Now-a-days Using the high power computing and information technology enables to collect store and process complex Marketing data. Data mining is used to extract knowledge from this marketing data. This report discuss about Data mining process, short discussion about different mining techniques such as classification tree, neural network, Regression and their application in marketing domain. My report Also cover different type of analyzes and tasks being used Introduction From the given topics I have selected the topic Data mining and Knowledge discovery for marketing since my cup of tea is Business and computing. I would always like to do research in Business analytics .Well let us look at what is data mining Data mining is the process of discovery of interesting, meaningful and actionable patterns hidden in large amounts of data . This is one of the tools to transform data into information. It is widely used in almost all fields of science and business profiling practice such as marketing, fraud detection, and scientific discovery. The technique to uncover pattern on data can also apply on sample data .so the sample data should be so the sample should be a good representative of larger data set. data mining can not find out the pattern which may be present in larger body of data and not contains in the small sub set of data. So this is very useful when sufficiently represented data are collected Most well known branches of data mining is knowledge discovery or KDD It derives knowledge from input data .This knowledge which have got from the process will become additional data and can be used for further discovery in related field normally an analyst can analysis and predict it.DM can generate thousands of pattern but all these patterns are not interested and useful. In this I am considering Data mining in a marketing field prospective. The data coming from different sources like transactions, loyalty cards, and discount coupons; customer complaint calls public life style studies using this data we can make Target marketing like to identify appropriate customer segments for new marketing initiatives determine customer purchasing pattern over time associations/co-relations between product sales, predict based on such association I mean cross market analysis what type of customer buys what type of product that is customer profiling Predict likelihood of customer churn and target those likely to leave with retention campaigns Customer requirement analysis like Identify the best products for different groups of customers and Predict what factors will attract new customers Provision of summary information such as Multidimensional summary reports and Statistical summary information (data central tendency and variation) Another question is why can not we go for a traditional data analysis instead of data mining? Answer is the field like marketing has tremendous Amount of data and it has multi dimension and complexity.A Marketing firm would likely to segment their customers into similar groups or clusters in order to better understand consumer behavior and more effectively market their products. In the past for a small business initiatives did not have trouble to understand their customers. They knew what they have to do once a customer approach them .Todays business is more competitive, more customer oriented, more products oriented so it is very difficult to understand the customer behavior, wants, needs the hidden relation ship between the data and preferences. With the help of data mining an analyst can deliver timely, personalized promotional offers. 1 Knowledge Discovery (KDD) Process S2 S1 S3 Data Cleaning Data Integration Databases Data Warehouse Knowledge Task-relevant Data Selection Data Mining Pattern Evaluation Normally in the huge DWH data mining environment data coming from various sources integrated and put it in data warehousing. Various data mining soft wares like teradata intelligent miners are used to mine Tera bytes of data and find market prediction. As I mentioned the DM is a Tools for developing predictive and descriptive models. Some are statistical method such as regression. Other use non statistical method like neural networks, classification trees. Here I considered some important tools then their How Classification trees are being used in marketing data mining Classification tree partition the data to maximize the difference in the dependent variable. it is also called a decision tree. Aim of classification tree is to classify the data into distinct groups or branches that create the strongest separation in the values of the dependent variables.The tree can identify segments. This can be helpful when a company is trying to understand what is driving market behavior. It detects nonlinear relationship. Mailed 10000 2.6% Male 4677 3.2% Female 2.15 2.1 % 1.7%  £30-45 3.6% > £45 4.1% Age>40 4.3% Age 0.7% Box shows resp rate in percentage The tree growth is through series of steps and rules .say for example sales pieces were mailed to 100000 names and yielded a response rate of 2.6%.the first split is on gender. This indicates that greatest difference between responders and non responders is gender. We see that males are much more responsive than females. We would consider males the better target group If we stop after one split. Our goal is to find out group with in both genders that discriminates between responders and non responders. In the next level split male and female groups are considered separately The second level split from the male node is on income, this implies that the income level varies in most between responders and non responders among the males. For female greatest difference is among the age group .It is very easy to identify the group with the highest response rate. Lets say that management decides to mail only to groups where the response rate is more than 3.5%.the offers would be directed to males who makes more than  £30000 a year and female over age 40 Some typical Classification tree Algorithms are 1) C4.5: Quinlan, J. R. C4.5: Programs for Machine Learning. Morgan Kaufmann., 1993. 2) CART: L. Breiman, J. Friedman, R. Olshen, and C. Stone. Classification and Regression Trees. Wadsworth, 1984 Linear regression and its applicability in marketing Knowledge of deviation from normal is very important for a marketer. In the past such deviations were very difficult to detect. Now-a-days data mining tools give great flexibility to detect and classify these changes. It is a statistical technique that quantifies the relationship between dependent variable and the independent variable, these are continuous. Consider the below equation, it shows a relation ship between sales and advertising along the regression equation .Our goal is to predict the sales based on the amount spend on advt. Plot a graph sales vs. advt that would be linear. A key measure of the strength of the relationship is the R-square. It measures the amount of overall variation in data that explained by the model. More than 70% Of the variation in sales can be explained by variation in advertising. Some times the relationship between sales and Advt is non linear (may be curvilinear) .By using the square root of advertising we are able to find better fit for the data. Sales=17.813+.0897*Advertising  £120  £1,503  £160  £1,755  £205  £2,971  £210  £1,682  £225  £3,497  £230  £1,998  £290  £4,598  £315  £2,937  £375  £3,622  £390  £4,402  £440  £3,844  £475  £4,470  £490  £5,492MINIMIZE SQUARED ERROR Advt sales ADVEGRTISIN -Æ’Â   x axis When building targeting models for marketing, risk and CRM, it is common to have much predictive variable. Using multiple predictive or independent continuous variables to predict a single continuous variable is called multiple linear regression .Targeting model created using linear regression is generally very robust. In marketing they can be used alone or in combination with other model. Neural Networks and its applicability in marketing Neural network does not follow any statistical distribution (Neural network is very vast topic a complete discussion is beyond the scope of this report) .it is modeled after the function of the human brain. The process is one of pattern recognition and error minimization. we can say it as nodes that are arranged in layers. The figure tells simple neural network with one hidden layer. Data has been classified into training and testing set (before the process).Then weight or input is assigned to each of the nodes in the first layer. During each iteration ,the input are processed through the system and compared to the actual value .the error is measured and fed back through the system to adjust the weights. The weights get better at predicting the actual results. A error limit is defined and it check with the error limit the process finishes when the minimum error limit reached One specific type of neural network commonly used in marketing uses sigmoidal functions to fit each node. This technique is very powerful in fitting a binary or twoilevel outcome such as response to an offer or a default on a loan Neural network not only pick linear data but also do a good pick up with non linear relation ship in the data. So this allows fitting data which is not possible to fit using regression. One disadvantage we can say that the result of neural net work is some what difficult to interpret A brief description on how Clustering can applicable in data mining Cluster analysis Cluster analysis group respondents with similar behaviors, preferences, or characteristics into segments. By doing so we can understand important similarities and differences between the respondents. Analyst can use this information to develop targeted marketing strategies, or to provide subgroups for analysis. In market survey data, clustering enables market researchers to group respondents who provide similar responses on several questions. In Clustering we use more than one variable that analyzes responses to several questions in order to find similar respondents. Clustering is based on the concept of creating groups based on their proximity to, or distance from, each other. Respondents within a cluster, therefore, are relatively homogenous. Most widely used Algorithms are 1)K-Means: MacQueen, J. B., Some methods for classification and analysis of multivariate observations, in Proc. 5th Berkeley Symp. Mathematical Statistics and Probability, 1967 2) BIRCH: Zhang, T., Ramakrishna, R., and Livny, M. 1996. BIRCH: an efficient data clustering method for very large databases. In SIGMOD 96 Let us look at some more major areas of application of data mining in the marketing like Customer profiling, Deviation analysis and Trend analysis. The pattern which formed after mining the data helps in analytics. Customer profiling This help to predict several marketing decision. A customer profile is a model of customer based on this marketer decides on the right strategies and tactics to meet the needs of that customer .The data mining task used in customer profiling can be dependency analysis, class identification and concept description. Below giving set of transaction that can help marketer to construct useful customer profiles. Frequency of purchases Marketing firm can build targeted promotion offer such as frequent buyer programs by looking how often their customer purchases product from their shop. Rcency of purchases The meaning of term is How long has it been since this customer last placed an order? Suppose a customer frequently visit the shop.It has been found that the specific customer or customer group not visiting the firm over long period of time .Market investigate the reason. By knowing this they can take appropriate offer or action. Size of purchases It tells, on a particular transaction how much he or she spends. This information helps to give resources to those customer groups. Identifying typical customer groups It gives characteristics of each group .For example a profile indicating that the customer has purchased a WINDOWS 7 SOFTWARE CD may hold to the marketer offering a special deal for MICROSOFT OFFICE SOFTWARE CD. Prospecting Customer profiles like buying patterns, give clues to the marketer on prospective customers. Say for example, consider the pattern Purchase of Norton Anti Virus package with one year validity is followed by purchase of Norton Up gradation version /or new version within 11 months about 85% of the time by high income customers discovered by data mining. Analyst who analysis pattern can identify the prospective customers for Upgraded/new version based on first time purchase details and tailor the mail catalog accordingly, thus, increasing the prospect of sales. 2 Deviation analysis Deviation analysis is one of the important analysis for example a higher than normal credit purchase on a credit card can be a fraud anomaly or a genuine purchase by the customer changes.Once a deviation has been discovered as a fraud, the marketer takes appropriate steps to prevent such frauds and initiates corrective action.If the deviation has been discovered as a change, further information collection is necessary. For example, a change can be that a customer got a new job and moved to a new house. In this case, the marketer has to update the knowledge about the customer. 3) Trend analysis Trends are patterns that persist over a period of time. Trends could be short-term trends like the immediate increase and subsequent slow decrease of sales following a sales campaign. Or, trends could be long-term, like the slow flattening of sales of a product over a few years. Data mining tools, such as visualization, help us detect trends, sometimes very subtle and hidden in the database, which would have been missed using traditional analysis tools like scatter plots. In marketing decisions, trends can be used for evaluating marketing programs or to forecast future sales. Data mining task in marketing data mining domain These tasks present in all data mining process we are just looking it into marketing prospective Dependency analysis Data Visualization Class identification Deviation Detection Concept Description Dependency analysis The market basket analysis gives the relationship between different product purchased by a customer .Using this techniques we can develop marketing strategy for promoting product that have dependency relationship in customers mind. Class identification It groups customers into classes which are defined in advance. Mathematical taxonomy and clustering are being used for class identification task. What the first one does is it maximizes the similarity with in classes but minimize similarity between classes. In clustering approach it determine the clustering according to attribute similarity as well as conceptual cohesiveness as defined by domain knowledge (describe above). A company doing business over the net, based on the session log data of internet users, the firm can classify the web users into email only users Surfers or Just for fun Surfer etc Concept description Comparison analysis will be done using statistical techniques. Using this we can compare marketing and customer knowledge. Deviation detection This helps us to determine the anomaly and changes. We can find the anomaly from various statistical techniques. This is already being explained above. Data visualization This kind of softwares allows the market research team or concerned people to view complex 3-D and 2-D patterns. They also provide drill down drill up slice facilities. In the KDD (knowledge discovery from data base) process, data visualization is used in association with other tasks such as dependency analysis, class identification, deviation detection and clustering. IBM SPSS PASW has got good data visualization techniques. Some of them are explained in Part 1 of the report. Conclusion Report discussed about Data mining process,

Midnights Children essay :: essays research papers

Midnight’s Children essay Salman Rushdie's creation, Saleem Sinai, has a self-proclaimed "overpowering desire for form" (363). In writing his own autobiography Saleem seems to be after what Frank Kermode says every writer is a after: concordance. Concordance would allow Saleem to bring meaning to moments in the "middest" by elucidating (or creating) their coherence with moments in the past and future. While Kermode talks about providing this order primarily through an "imaginatively predicted future" (8), Saleem approaches the project by ordering everything in his past into neat, causal relationships, with each event a result of what preceded it. While he is frequently skeptical of the true order of the past, he never doubts its eminence; he is certain that everyone is "handcuffed to history" (482). His belief in the preeminence of the past, though, is distinctly different than the reality of time for the Saleem who emerges through that part of the novel that Gerard Gen ette calls "the event that consists of someone recounting something" (26) (Saleem-now, we can call this figure). Saleem-now is motivated to act not by the past, but instead by the uncertainty and ambiguity of the future. Saleem's construction of his own story is an effort to mitigate the lack of control he feels in looking toward the unknown future. To pacify himself he creates a world that is ordered but this world is contrary to his own reality. Saleem spends much of his energy in the story setting up neat causal relationships between events in his past to demonstrate his place "at the center of things" (272). He carefully mentions his tumble into the middle of a parade for the partition of Bombay and then proceeds to propose that "in this way I became directly responsible for triggering off the violence which ended with the partition of the state of Bombay" (219). When telling us of his school-mate Cyrus disappearance from school and emergence as a great religious prophet Saleem quickly mentions the Superman comics that he had given Cyrus earlier, and attributes Cyrus' rise to prophetdom as a direct response to these comics. By viewing Cyrus' motivation in this way Saleem says "[I] found myself obliged, yet again, to accept responsibility for the events of my turbulent, fabulous world" (309). There is an obvious note of skepticism toward these most overt acts of placing himself at the center of things. At one point he asks himself "am I so far gone, in my desperate need for meaning, that I'm prepared to distort everything†¹to re-write the whole history of my times purely in order to place myself in a central role?

Bursitis :: essays research papers fc

Bursitis Does it hurt to move your arm? Is it tender and radiating pain to your neck and finger tips? Do you have a fever? If you answered yes to two or more of these questions then you may have typical joint injury called bursitis. Bursitis is an inflammation of the bursa that is easily prevented, detected and treated. Bursitis is a common condition that can cause much pain and swelling around an affected bursa. A bursa is a sac between body tissues that move against each other. They are filled with a lubricating liquid to minimize the fiction between the tissues. The bursa are found mostly in joints between skin and bone or bone and tendons. When you irritate these lubricating sacs, the bursae fill with fluid and become irritated and inflamed. This inflammation causes severe pain with movement of the joint, often limiting the movement of the affected area. Bursitis commonly strikes in the shoulders, elbows, knees, pelvis, hips or Achilles tendons. Bursitis can affect nearly anyone for any number of reasons. It affects mainly adults both male and female. The individuals most at risk are people who engage in excessive and improper stretching and people who are involved heavily in athletic training. Bursitis can be caused by many things. For one, it can be caused by injury or overuse of a joint. Strenuous unfamiliar exercise also can cause Bursitis. Plus, such diseases as gout, arthritis, and chronic infection of a joint can be likely causes. But frequently the cause of Bursitis can not be determined. The only ways to prevent getting it are to wear protective gear when exorcising, practice appropriate warm ups and cool downs during exercise and to maintain a high fitness level. Bursitis is an easily treatable disease. If you suspect that you have bursitis, you will probably seek the advice of a doctor. Most likely the doctor will look at your medical history and take some x-rays. If you are diagnosed with bursitis the doctor may prescribe some non-steroidal anti-inflammatory drugs and/or pain relievers and may make some cortisone injections into the bursa to relieve inflammation. Once at home you are expected rest the affected area as much as possible and to apply RICE ( rest, ice, compression and elevation of the inflamed joint). Also to prevent the joint from freezing you should begin moving and exercising the affected area as soon as possible. Most likely the problem will subside in 7 to

Lenin’s View of Economic Policies in Russia Essay

Lenin’s view of economic polices in Russia between 1917 and 1923 was shaped by the factors of War Communism, and the New Economic Plan (NEP). Lenin realized that to have a successful economy and to keep the idea of equality in Communism there had to be a compromise; there needed to be a balance of state control and individual incentive for the economy to prosper. Through the failure of War Communism and the success of the economy and the drift away from Communism with the NEP; Lenin learned the ‘do’s and don’ts’ of a successful economy. Lenin described what the country needed to do to have a successful economy, he said, † We have found that a degree of private commercial interest, with state supervision and control of that interest, is all we actually need†¦ (doc. 5). This shows how both War Communism and the NEP were factors that shaped Lenin’s compromising idea of what a economy needed to be successful. The War Communism policy was adopted to keep the Red Army supplied. During War Communism the government took control of industry, and told factories what to produce; and the government would take any grain that was produced by the farmers. The Cheka would steel the grain that the farmers produced, this made the farmers angry and they no longer had any incentive to grow crops because the crops would just be taken away from them. Also money became worthless, wages were paid in food or other goods, and many people bartered goods instead of using money. The situation for the farmers and the peasants got worse. By 1921 Russia’s economy was shattered. Industrial production had drastically decreased; and the cities were in chaos. Agriculture had been demolished. War Communism was put in affect to increase the productivity of both industrial goods and food, but the workers and farmers saw no point in putting in the effort if in the end it would be taken away from them. War Communism led to the destruction of the economy of Russia. Lenin finally admitted that War Communism was a mistake, he said, â€Å"The small farmer needs a spur, and incentive that accords with his condition†¦ We are very much to blame for having gone to far; we overdid the nationalization of industry and trade, clamping down on the local exchange of commodities. Was that a mistake? It certainly was. (doc. 4)† This quote is an example of how Lenin realized that he had made a mistake and this quote also shows that Lenin understands that the workers and farmers need an incentive to work; with an incentive the economy will grow. Lenin’s view of economic policies was greatly influenced by the failure of War Communism, and by the failure he was able to figure out another system that would revive Russia’s economy. Lenin realized that to have a successful economy the people have to have the incentive to work. Lenin also knew that if he did not improve the economic state of Russia that the Communists would not survive; War Communism took the ‘safety net’ away from the Communists. Lenin had to act quickly to figure out another policy that would make the people want to work, and to revive the economy. In 1921 the NEP was created to fix the economy. Lenin created this new policy to try to burst the morale of the people and make them want to produce more grain or products. The transition form War Communism to the NEP was drastic, the people had to change there lives to fit this new, more capitalists society. Though the change to a more capitalist economy, the NEP was successful in ‘jump starting’ the farming production, for example. Lenin says, â€Å"Release of [surplus goods] into circulation would stimulate small farming, which is in terrible state†¦Ã¢â‚¬  (doc. 4) This is a great example of how Lenin realizes why he needs to change from the War Communism to the NEP, for a least one reason to increase the amount of grain produced. The creation of the NEP changed many laws that were once holding Russia’s economy back. Grain requisitioning was stopped. Grain was no longer taken from the peasants. Also traders could buy and sell goods, which was illegal during War Communism. Smaller factories were returned to their former owners; and they were allowed to sell the goods they made and make a profit from them. Finally larger industries like coal and steel remained under state control; but some larger factories were able to sell their products. These were some of the main differences between War Communism and NEP. During the NEP the economy prospered, because people were now allowed to keep some of the goods they made and then sell them for their own profit. This made the people want to produce more so they could have more for their family. Lenin described some of the good affects that the NEP had, he said, â€Å"We have achieved much with our requisitioning system. Our food policy has made it possible in the second year to acquire three times as much grain as in the first.† (doc. 2) Lenin was talking about the great increase in the production of grain, this great increase was directly related to the NEP; because the peasants could keep some of the grain they made which gave them an incentive to work hard. Communists were angry because they saw the country returning to capitalism. They did not like the fact that bosses of factories called kulacks could hire men to work for them. Also Communists disliked the ‘Nepmen’, because they made a high profit by buying goods cheaply and then selling them for more. Though the NEP revived the economy, people, especially peasants were unhappy with the new capitalist society. Lenin’s view of economic policies was changed through the NEP, he knew that people need the incentive to work, but he also knew he could not give the people to much economic freedom; the idea of balancing the policy of War Communism and NEP was Lenin’s final view of how to keep the people happy and to sustain a great economy.

Gestalt Learning Theory Essay

Doing my research on learning and instruction in complex simulation-based learning environments, I experienced a large difference in how learners reacted to my learning material (Kluge, in press, 2004). Complex technical simulations involve the placement of the learner into a realistic computer simulated situation or technical scenario which puts control back into the learner’s hands. The contextual content of simulations allows the learner to â€Å"learn by doing. † Although my primary purpose was in improving research methods and testing procedures for evaluating learning results of simulation-based learning, the different reaction of our participants were so obvious that we took a closer look. I had two different groups participating in my learning experiments: students from an engineering department at the University, mostly in their 3rd semester, and apprentices from vocational training programs in mechanics and electronics of several companies near the University area in their 3rd year of vocational training. Most of the students worked very intensively and concentrated on solving these complex simulation tasks whereas apprentices became easily frustrated and bored. Although my first research purpose was not in investigating the differences between these groups, colleagues and practitioners showed their interest and encouraged me to look especially at that difference. Practitioners especially hoped to find explanations why apprentices sometimes are less enthusiastic about simulation learning although it is said to be motivating for their perception. Therefore, in this dissertation I address the difference in the effectiveness of using simulation intervention program based on a Gestalt learning theory. Moreover, to find out if the program improves either or both the quality and speed of the learning process of students enrolled in a highly technical training program. This dissertation focuses on using simulation based learning environments in vocational training program. In this chapter, the experimental methodology and instruments are described, results presented and finally discussed. As mentioned above, my primary purpose when I started to investigate learning and simulation based on Gestalt learning theory was focused on improving the research methodology and test material (see Kluge, in press, 2004) for experimenting with simulation-based learning environments. But observing the subjects’ reactions to the learning and testing material the question arose whether there might be a difference in the quality of and speed of the learning process of students involved in my study. Research Design: A 3-factor 2 ? 2 ? 2 factorial control-group-design was performed (factor 1: â€Å"Simulation complexity†: ColorSim 5 vs ColorSim 7; factor 2: â€Å"support method†: GES vs. DI-GES; factor 3: target group, see Table 2). Two hundred and fifteen mostly male students (16% female) in eight groups (separated into four experimental and four control groups) participated in the main study. The control group served as a treatment check for the learning phase and to demonstrate whether subjects acquired any knowledge within the learning-phase. While the experimental groups filled in the knowledge test at the end of the experiment (after the learning and the transfer tasks), the control groups filled in the knowledge test directly after the learning phase. I did not want to give the knowledge test to the experimental group after the learning phase because of its sensitivity to testing-effects. I assumed that learners who did not acquire the relevant knowledge in the learning phase could acquire useful knowledge by taking the knowledge test, which could have led to a better transfer performance which is not due to the learning method but caused by learning from taking the knowledge test. The procedure subjects had to follow included a learning phase in which they explored the structure of the simulation aiming at knowledge acquisition. After the learning phase, subjects first had to fill in the four-item questionnaire on self-efficacy before they performed 18 transfer tasks. The transfer tasks were separated into two blocks (consisting of nine control tasks each) by a 30-minute break. In four experimental groups (EG), 117 students and apprentices performed the learning phase (28 female participants), the 18 control tasks and the knowledge test. As said before, the knowledge test was applied at the end because of its sensitivity to additional learning effects caused by filling in the knowledge test. In four control groups (CG), 98 students and apprentices performed the knowledge test directly after the learning phase, without working on the transfer task (four female participants). The EGs took about 2-2. 5 hours and the CG about 1. 5 hours to finish the experiment. Both groups (EGs and CGs) were asked to take notes during the learning phase. Subjects were randomly assigned to the EGs and CGs, nonetheless ensuring that the same number of students and apprentices were in each group. The Simulation-Based Learning Environment The computer-based simulation ColorSim, which we had developed for our experimental research previously, was used in two different variants. The simulation is based on the work by Funke (1993) and simulates a small chemical plant to produce colors for later subsequent processing and treatment such as dyeing fabrics. The task is to produce a given amount of colors in a predefined number of steps (nine steps). To avoid the uncontrolled influence of prior knowledge, the structure of the plant simulation cannot be derived from prior knowledge of a certain domain, but has to be learned by all subjects. ColorSim contains three endogenous variables (termed green, black, and yellow) and three exogenous variables (termed x, y, and z ). Figure 1 illustrates the ColorSim screen. Subjects control the simulation step by step (in contrast to a real time running continuous control). The predefined goal states of each color have to be reached by step nine. Subjects enter values for x, y, and z within the range of 0-100. There is no time limit for the transfer tasks. During the transfer tasks, the subjects have to reach defined system states for green (e. g. , 500), black (e. g. , 990), and yellow (e. g. , 125) and/or try to keep the variable values as close as possible to the values defined as goal states. Subjects are instructed to reach the defined system states at the end of a multi-step process of nine steps. The task for the subjects was first to explore or learn about the simulated system (to find out the causal links between the system variables), and then to control the endogenous variables by means of the exogenous variables with respect to a set of given goal states. With respect to the empirical evidence of Funke (2001) and Strau? (1995), the theoretical concept for the variation in complexity is based on Woods’ (1986) theoretical arguments that complexity depends on an increasing number of relations between a stable number of (in this case six) variables (three input, three output: for details of the construction rational and empirical evidence see Kluge, 2004, and Kluge, in press, see Table 1). To meet reliability requirements, subjects had to complete several trials in the transfer task. For each of the 18 control tasks a predefined correct solution exists, to which the subjects’ solutions could be compared. In addition, knowledge acquisition and knowledge application phases were separated. The procedure for the development of a valid and reliable knowledge test is described in the next section. Different methods have been developed to provide learners with support to effectively learn from using simulations. De Jong and van Joolingen (1998) categorize these into five groups: 1. Direct access to domain knowledge, which means that learners should know something about the field or subject beforehand, if discovery learning is to be fruitful. 2. Support for hypothesis generation, which means learners are offered elements of hypotheses that they have to assemble themselves. 3. Support for the design of experiments, e. g. , by providing hints like â€Å"It is wise to vary only one variable at a time† 4. Support for making predictions, e. g. , by giving learners a graphic tool in which they can draw a curve that gives predictions at three levels of precision: as numerical data, as a drawn graph, and as an area in which the graph would be located. 5. Support for regulative learning processes: e. g. , by introducing model progression, which means that the model is introduced gradually, and by providing planning support, which means freeing learners from the necessity of making decisions and thus helping them to manage the learning process. In addition, regulative processes can be supported by leading the learner through different stages, like â€Å"Before doing the experiment . . . ,† â€Å"Now do the experiment,† â€Å"After doing the experiment. . . .† Altogether, empirical findings and theoretical assumptions have so far led to the conclusion that experiential learning needs additional support to enhance knowledge acquisition and transfer. Target Population and Participant Selection: In the introductory part, I mentioned that there were two sub groups in the sample which I see as different target groups for using simulation-based learning environments. Subjects were for the most part recruited from the technical departments of a Technical University (Mechanical Engineering, Civil Engineering, Electronics, Information Technology as well as apprentices from the vocational training programs in mechanics