1. GROUP 2

2. GROUP MEMBERSSEXREG NUMBER 1.LAWRENCE TEBANDEKEMale2013/BIT/013 2.MUSA TEBANDEKEMale2013/BIT/014 3.AFUA ANKWASAFemale2013/BIT/020/PS 4.AGGREY ATAMBAMale2013/BIT/021/PS 5.AISHA NANSAMBAFemale2013/BIT/025/PS 6.ALEX TWESIGYEMale2013/BIT/027/PS 7.ALLAN KAMUGISHAMale2013/BIT/028/PS 8.AMBROZE TWINAMASTIKOMale2013/BIT/029/PS 9.AMON KAKURUMale2013/BIT/030/PS 10.AMOS ANDINDAMale2013/BIT/031/PS 11.ANDREW K KYAMANYIMale2013/BIT/034/PS 12.ANNET KOBUSINGYEFemale2013/BIT/037/PS

3. We acknowledge National Association of State Chief Information Officers (Nascio), this work is a part of their research efforts Purpose and Uses of Data Mining

4. Definition Data mining is defined as extracting information from huge sets of data and summarizing it into new useful information.

5. Purpose and Uses of Data Mining The purpose of data mining is to identify patterns in order to make predictions from information contained in databases. it allows the user to be proactive in identifying and predicting trends with that information. Below is the purpose and the uses of data mining;

6. Market Analysis and management Fraud Detection Customer Retention Production Control Science Exploration

7. Market Analysis and management Listed below are the various fields of market where data mining is used Customer Profiling - Data mining helps determine what kind of people buy what kind of products. Identifying Customer Requirements - Data mining helps in identifying the best products for different customers. It uses prediction to find the factors that may attract new customers. Cross Market Analysis - Data mining performs Association/correlations between product sales.

8. Market Analysis and management cn’t Target marketing - data mining helps to find clusters of model customers who share the same characteristics such as interests, spending habits, income, etc. Determining customer purchasing pattern - data mining helps in determining customer purchasing pattern. Providing summary information - data mining provides us various multidimensional summary reports.

9. Market Analysis and management cn’t Finance planning and asset evaluation - it involves cash flow analysis and prediction, contingent claim analysis to evaluate assets. Resource planning - it involves summarizing and comparing the resources and spending. Competition - it involves monitoring competitors and market directions.

10. Fraud Detection Data mining is also used in the fields of credit card services and telecommunication to detect frauds. In fraud telephone calls, it helps to find the destination of the call, duration of the call, time of the day or week, etc. It also analyzes the patterns that deviate from expected norms.

11. Customer Retention In a world where price wars occur, you will get customers jumping ship every time a competitor offers lower prices. You can use data mining to help minimize this churn, especially with social media. Spigit uses different data mining techniques from your social media audience to help you acquire and retain more customers.

12. Production Control Data mining is also perfect for creating custom products designed for market segments. In fact, you can predict which features users may want…although truly innovative products are not created from giving customers what they want. Rather truly innovative products are created when you look at the data from your customers and spot holes customers are demanding be filled. When it comes to creating that product, these are the elements that must be baked into the product.

13. Science Exploration Rules generated by data mining are empirical - they are not physical laws. In most research in the sciences, one compares recorded data with a theory that is founded on an analytic expression of physical laws. The success or otherwise of the comparison is a test of the hypothesis of how nature works expressed as a mathematical formula. This might be something fundamental like an inverse square law.

14. Science Exploration 2 In a growing number of domains, the empirical or black box approach of data mining is good science. Three typical examples are: 1.Sequence analysis in bioinformatics 2.Classification of astronomical objects 3.Medical decision support

15. Privacy Implications As data mining has evolved, its impact on privacy has become increasingly complex and controversial. Data mining technologies initially assisted the user in accessing and reducing large amounts of information. However, the factors listed below have made addressing privacy in relation to data mining much more difficult:

16. 1.Increased availability and decreased cost of data mining tools 2.Increased digitization of data and consequential increase in the amount of data 3.Available and inability of humans to manually process relationships in data without computer assistance 4.Increased data aggregation 5.Increased ability of data mining tools to extract patterns that go beyond actual data and that attempt to predict repetitive behavior and data value patterns 6.Increased use of data warehouses as central repositories for multiple applications.

17. Personal Information The privacy implications of data mining technologies tend to be two-fold. First, the mining of personal information has raised privacy concerns. For purposes of its data mining study, GAO (Government Accountability Office) considered “personal information” to be “all information associated with an individual and includes both identifying and non identifying information.” Examples of identifying information which can be used to locate or identify an individual include an individual’s name, aliases, social security number, e-mail address, driver’s license number, and agency- assigned case number. Non-identifying personal information includes an individual’s age, education, finances, criminal history, physical attributes, and gender. the main concern with aggregating such personal information and mining it is that profiles of individuals can be created using information held in disparate systems located both in the commercial and government sectors.

18. IDENTIFICATION OF TERRORISTS AND CRIMINALS: Another set of privacy issues are raised when data mining is used to identify individuals involved in terrorist or criminal activity or to determine if an already-identified suspect has a pattern of being involved in criminal or terrorist activities. Since data mining can be used to predict which individual might commit a crime, those using data mining for that purpose must be careful to put in place requirements that detail when action may be taken against an individual as the result of data mining activities and what is done with mined information that is subsequently determined not to be relevant to an investigation.

19. Lessons Learned: The privacy implications of data mining recently have become much more high profile with controversies over TIA (Terrorism Information Awareness) program, CAPPS II (Computer Assisted Passenger Prescreening System), and MATRIX (Multistate Anti-Terrorism Information Exchange). These programs have raised concerns about the collection of personal information by the government and the subsequent mining of that information.

20. Business where data mining is crucial for its success Let us look at a super market business The supermarket can Increase customer loyalty, Unlock hidden profitability, Reduce client churn, etc if they deploy the following;

21. 1. Basket Analysis Sometimes called “affinity analysis,” this looks at the items that a customer bought, which could help the super market improve their stock layouts. It’s based on the assumption that you can predict future customer behavior by past performance, including purchases and preferences. This can be done through; 1.Evaluating use of credit cards. 2.Evaluating patterns of telephone use.

22. 2. Sales Forecasting This looks at when customers bought, and tries to predict when they will buy again. You could use this type of analysis to determine a strategy of planned obsolescence or figure out complimentary products to sell.

23. 3. Database Marketing Database marketing is the process of identifying, collecting and then analyzing relevant information about a company’s customers. The database is compiled using data obtained from a range of internal and external sources such as sales information, email correspondence, warranty cards, promotional efforts and, now more than ever, social media. The primary aim of database marketing is to then use the information within the database to implement marketing strategies that ultimately increases profits.

24. By examining customer purchasing patterns and looking at the demographics and psychographics of customers to build profiles, you can create products that will sell themselves.

25. 4. Merchandise Planning The company is looking to grow, by adding stores can evaluate the amount of merchandise they will need by looking at the exact layout of a current store.

26. 5. Card Marketing Here we can collect the information from usage, identify customer segments and then based on information on these segments build programs that improve retention, boost acquisition, target products to develop and design prices.

27. 6. Market Segmentation One of the best uses of data mining to the super market business is to segment their customers. And it’s pretty simple. From their data they can break down their market into meaningful segments like age, income, occupation or gender.

28. 7. Product Production Data mining is also perfect for creating custom products designed for market segments. In fact, we can predict which features users may want…although truly innovative products are not created from giving customers what they want. Rather truly innovative products are created when you look at the data from your customers and spot holes customers are demanding be filled.

29. Using an example of the hardware database, below are the data mining functionalities. Data Characterization - This refers to summarizing data of a class under study. This class under study is called as the Target Class. For example, characteristics for customers who have bought more than 100 cement bags last year. The result can be a general profile such as age, employment status or credit ratings. Data mining functionalities

30. Data Discrimination - It is a comparison of the general features of targeting class data objects with the general features of objects from one or a set of contrasting classes. User can specify target and contrasting classes. The user may like to compare the general features of the customers whose cement purchase has increased by 40% in the last year with those whose cement purchase has decreased 30% in the same duration.

31. Mining of Association Associations are used in retail sales to identify patterns that are frequently purchased together. This process refers to the process of uncovering the relationship among data and determining association rules. For example, a hardware generates an association rule that shows that 70% of time iron sheets are sold with 4 inch silver nails and only 30% of times 5 inch nails are sold with iron sheets.

32. Mining of Correlations It is a kind of additional analysis performed to uncover interesting statistical correlations between associated- attribute-value pairs or between two item sets to analyze that if they have positive, negative or no effect on each other.

33. Mining of Clusters Cluster refers to forming group of objects that are very similar to each other but are highly different from the objects in other clusters. Example: Cluster analysis can be performed on customer data in order to identify homogeneous subpopulations of customers. These clusters may represent individual target groups for marketing. The figure on next slide shows a plot of customers with respect to customer locations in a city.

35. Classification and Prediction Classification is the process of finding a model that describes the data classes or concepts. The purpose is to be able to use this model to predict the class of objects whose class label is unknown. This derived model is based on the analysis of sets of training data(a training set is a set of data used to discover potentially predictive relationships). The derived model can be presented in the following forms: Classification (IF-THEN) Rules Decision Trees Mathematical Formulae Neural Networks

36. Classification cnt’d

37. The list of functions involved in these processes are as follows: Classification - It predicts the class of objects whose class label is unknown. Its objective is to find a derived model that describes and distinguishes data classes or concepts. Prediction - It is used to predict missing or unavailable numerical data values rather than class labels. Regression Analysis is generally used for prediction. Prediction can also

38. Outlier Analysis - A database may contain data objects that do not comply with the general behavior or model of the data. These data objects are outliers. Example: Use in finding Fraudulent usage of credit cards. Outlier Analysis may uncover Fraudulent usage of credit cards by detecting purchases of extremely large amounts for a given account number in comparison to regular charges incurred by the same account. Outlier values may also be detected with respect to the location and type of purchase or the purchase frequency.

39. Evolution Analysis Evolution analysis refers to the description and model regularities or trends for objects whose behavior changes over time. Example: Time-series data. If the stock market data (time-series) of the last several years available from the Ugandan Stock exchange and one would like to invest in shares of high tech industrial companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing to one’s decision making regarding stock investments.

40. Any Questions?