1. Data Mining Adrian Tuhtan 004757481 CS157A Section1

2. Overview Introduction Explanation of Data Mining Techniques Advantages Applications Privacy

3. Data Mining What is Data Mining? “The process of semi automatically analyzing large databases to find useful patterns” (Silberschatz) KDD – “Knowledge Discovery in Databases” (3) “Attempts to discover rules and patterns from data” Discover Rules  Make Predictions Areas of Use Internet – Discover needs of customers Economics – Predict stock prices Science – Predict environmental change Medicine – Match patients with similar problems  cure

4. Example of Data Mining Credit Card Company wants to discover information about clients from databases. Want to find: Clients who respond to promotions in “Junk Mail” Clients that are likely to change to another competitor Clients that are likely to not pay Services that clients use to try to promote services affiliated with the Credit Card Company Anything else that may help the Company provide/ promote services to help their clients and ultimately make more money.

5. Data Mining & Data Warehousing Data Warehouse: “is a repository (or archive) of information gathered from multiple sources, stored under a unified schema, at a single site.” (Silberschatz) Collect data  Store in single repository Allows for easier query development as a single repository can be queried. Data Mining: Analyzing databases or Data Warehouses to discover patterns about the data to gain knowledge. Knowledge is power.

6. Discovery of Knowledge

7. Data Mining Techniques Classification Clustering Regression Association Rules

8. Classification Classification: Given a set of items that have several classes, and given the past instances (training instances) with their associated class, Classification is the process of predicting the class of a new item. Therefore to classify the new item and identify to which class it belongs Example: A bank wants to classify its Home Loan Customers into groups according to their response to bank advertisements. The bank might use the classifications “Responds Rarely, Responds Sometimes, Responds Frequently”. The bank will then attempt to find rules about the customers that respond Frequently and Sometimes. The rules could be used to predict needs of potential customers.

9. Technique for Classification Decision-Tree Classifiers Job Income Job Income Carpenter Engineer Doctor BadGoodBadGood BadGood <30K<40K<50K>50K>90K >100K Predicting credit risk of a person with the jobs specified.

10. Clustering “Clustering algorithms find groups of items that are similar. … It divides a data set so that records with similar content are in the same group, and groups are as different as possible from each other. ” (2) Example: Insurance company could use clustering to group clients by their age, location and types of insurance purchased. The categories are unspecified and this is referred to as ‘unsupervised learning’

11. Clustering Group Data into Clusters Similar data is grouped in the same cluster Dissimilar data is grouped in the same cluster How is this achieved ? K-Nearest Neighbor A classification method that classifies a point by calculating the distances between the point and points in the training data set. Then it assigns the point to the class that is most common among its k-nearest neighbors (where k is an integer).(2) Hierarchical Group data into t-trees

12. Regression “Regression deals with the prediction of a value, rather than a class.” (1, P747) Example: Find out if there is a relationship between smoking patients and cancer related illness. Given values: X1, X2... Xn Objective predict variable Y One way is to predict coefficients a0, a1, a2 Y = a0 + a1X1 + a2X2 + … anXn Linear Regression

13. Regression Example graph: Line of Best Fit Curve Fitting

14. Association Rules “An association algorithm creates rules that describe how often events have occurred together.” (2) Example: When a customer buys a hammer, then 90% of the time they will buy nails.

15. Association Rules Support: “is a measure of what fraction of the population satisfies both the antecedent and the consequent of the rule”(1, p748) Example: People who buy hotdog buns also buy hotdog sausages in 99% of cases. = High Support People who buy hotdog buns buy hangers in 0.005% of cases. = Low support Situations where there is high support for the antecedent are worth careful attention E.g. Hotdog sausages should be placed in near hotdog buns in supermarkets if there is also high confidence.

16. Association Rules Confidence: “is a measure of how often the consequent is true when the antecedent is true.” (1, p748) Example: 90% of Hotdog bun purchases are accompanied by hotdog sausages. High confidence is meaningful as we can derive rules. Hotdog bun  Hotdog sausage 2 rules may have different confidence levels and have the same support. E.g. Hotdog sausage  Hotdog bun may have a much lower confidence than Hotdog bun  Hotdog sausage yet they both can have the same support.

17. Advantages of Data Mining Provides new knowledge from existing data Public databases Government sources Company Databases Old data can be used to develop new knowledge New knowledge can be used to improve services or products Improvements lead to: Bigger profits More efficient service

18. Uses of Data Mining Sales/ Marketing Diversify target market Identify clients needs to increase response rates Risk Assessment Identify Customers that pose high credit risk Fraud Detection Identify people misusing the system. E.g. People who have two Social Security Numbers Customer Care Identify customers likely to change providers Identify customer needs

19. Applications of Data Mining (4) Source IDC 1998

20. Privacy Concerns Effective Data Mining requires large sources of data To achieve a wide spectrum of data, link multiple data sources Linking sources leads can be problematic for privacy as follows: If the following histories of a customer were linked: Shopping History Credit History Bank History Employment History The users life story can be painted from the collected data

21. References 1. Silberschatz, Korth, Sudarshan, “Database System Concepts”, 5 th Edition, Mc Graw Hill, 2005 2. http://www.twocrows.com/glossary.htm, “Two Crows, Data Mining Glossary” http://www.twocrows.com/glossary.htm 3. http://en.wikipedia.org/wiki/Data_mining, “Wikipedia” http://en.wikipedia.org/wiki/Data_mining 4. http://phoenix.phys.clemson.edu/tutorials/excel/regressi on.html http://phoenix.phys.clemson.edu/tutorials/excel/regressi on.html 5. http://wwwmaths.anu.edu.au/~steve/pdcn.pdf http://wwwmaths.anu.edu.au/~steve/pdcn.pdf