1. MAKING THE BUSINESS BETTER Presented By Mohammed Dwikat DATA MINING Presented to Faculty of IT MIS Department An Najah National University

2. Mohammed Dwikat Data Mining What is Data Mining  Exploration & analysis of large quantities of data in order to discover meaningful patterns  Extraction useful information from data  Group together similar documents returned by search engine

3. Mohammed Dwikat Data Mining What is NOT Data Mining  Look up phone number in phone directory  Query a Web search engine for information about “Amazon”  Search a customer name in a Bank

4. Mohammed Dwikat Data Mining Data Mining Tasks Predictive Methods Use some variables to predict unknown or future values of other variables. Descriptive Methods Find human-interpretable patterns that describe the data.

5. Mohammed Dwikat Data Mining Clustering [Descriptive] Association Rule Discovery [Descriptive] Sequential Pattern Discovery [Descriptive] Classification [Predictive] Regression [Predictive] Deviation Detection [Predictive] Data Mining Tasks

6. Mohammed Dwikat Data Mining Clustering Example Euclidean Distance Based Clustering in 3-D space Intracluster distances are minimized Intracluster distances are minimized Intercluster distances are maximized Intercluster distances are maximized

7. Mohammed Dwikat Data Mining Market Segmentation: Goal: subdivide a market into distinct subsets of customers where any subset may conceivably be selected as a market target to be reached with a distinct marketing mix. Document Clustering: Goal: To find groups of documents that are similar to each other based on the important terms appearing in them. Other Clustering Examples

8. Mohammed Dwikat Data Mining predict occurrence of an item based on occurrences of other items Association Rule Example Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer} Rules Discovered: {Milk} --> {Coke} {Diaper, Milk} --> {Beer}

9. Mohammed Dwikat Data Mining Marketing and Sales Promotion Supermarket shelf management Inventory Management Other Association Rule Examples

10. Mohammed Dwikat Data Mining Find rules that predict strong sequential dependencies among different events. Sequential Pattern Discovery Example (A B) (C) (D E)

11. Mohammed Dwikat Data Mining Other Sequential Pattern Discovery Examples In telecommunications alarm logs, (Inverter_Problem Excessive_Line_Current) (Rectifier_Alarm) --> (Fire_Alarm) In point-of-sale transaction sequences, Computer Bookstore: (Intro_To_Visual_C) (C++_Primer) --> (Perl_for_dummies,Tcl_Tk) Athletic Apparel Store: (Shoes) (Racket, Racketball) --> (Sports_Jacket)

12. Mohammed Dwikat Data Mining Classification Example Given a collection of records (training set ) Each record contains a set of attributes, one of the attributes is the class. Find a model for class attribute as a function of the values of other attributes. Goal: previously unseen records should be assigned a class as accurately as possible. A test set is used to determine the accuracy of the model.

13. Mohammed Dwikat Data Mining Classification Example categorical continuous class Test Set Training Set Model Learn Classifier

14. Mohammed Dwikat Data Mining Other Classification Examples Direct Marketing Reduce cost of mailing by targeting a set of consumers likely to buy a new cell-phone product Fraud Detection Predict fraudulent cases in credit card transactions.

15. Mohammed Dwikat Data Mining Regression Examples Predict a value of a given continuous valued variable based on the values of other variables, assuming a linear or nonlinear model of dependency. Examples: Predicting sales amounts based on advertising expenditure. Predicting wind velocities as a function of temperature, humidity, air pressure, etc. Time series prediction of stock market indices.

16. Mohammed Dwikat Data Mining Deviation/Anomaly Example Detect significant deviations from normal behavior Applications: Credit Card Fraud Detection Network Intrusion Detection

17. Mohammed Dwikat Data Mining Prediction Measurement Confusion Matrix Example of confusion matrix Predicted Actual PassFail Pass93 Fail17 True Positive vs. True Negative False Positive vs. False Negative

18. Mohammed Dwikat Data Mining Challenges Distributed Data Dimensionality Complex and Heterogeneous Data Data Quality Data Ownership and Distribution Privacy Preservation

19. Mohammed Dwikat Data Mining WEKA Free, Simple, Limited SAS Enterprise Miner Data Miner, Text miner SPSS Regression, Time Series and more Data Mining Applications

20. Mohammed Dwikat Data Mining Questions Thank You