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

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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

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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

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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.

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

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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

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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

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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}

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

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

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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)

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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.

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

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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.

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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.

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

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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

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

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

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Mohammed Dwikat Data Mining Questions Thank You

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