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Data Mining

3-2. Overview. Transaction DatabaseWhat is Data MiningData Mining PrimitivesData Mining ObjectivesPredictive ModelingKnowledge DiscoveryOther Objectives to Data MiningWhat Data Mining is NotOther Factors in Data Mining CategorizationConclusion. 3-3. Transaction Database. Relation Consisti

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Data Mining

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    1. 3-1 Data Mining Kelby Lee

    2. 3-2 Overview Transaction Database What is Data Mining Data Mining Primitives Data Mining Objectives Predictive Modeling Knowledge Discovery Other Objectives to Data Mining What Data Mining is Not Other Factors in Data Mining Categorization Conclusion

    3. 3-3 Transaction Database Relation Consisting of Transactions TID (Transaction Identifier) Regularities between Transaction Behavior

    4. 3-4 Transaction Database Table 1.1 Transaction Database TID Customer Item Date Price Quantity --------------------------------------------------------------------------------------------------------------------------------- 100 C1 chocolate 01/11/2001 1.59 2 100 C1 ice cream 01/11/2001 1.89 1 200 C2 chocolate 01/12/2001 1.59 3 200 C2 candy bar 01/12/2001 1.19 2 200 C2 jackets 01/12/2001 120.39 2 300 C3 jackets 01/14/2001 168.88 1 300 C3 color shirts 01/14/2001 27.95 2 400 C4 jackets 01/15/2001 149.49 1

    5. 3-5 Association Rules A customer who buys chocolate will likely buy candy bar one type of Data Mining task

    6. 3-6 Discovered Rules Table 1.2 Discovered Rules Rule Bought this... ...also bought that ------------------------------------------------------------------------------------------------- 1 chocolate ice cream 2 candy bar chocolate 3 ski pants colored shirt 4 beer diaper

    7. 3-7 What is Data Mining Retrieve individual elements Given a name of a product, find price and producer Analysis Average monthly sales amount and derivation

    8. 3-8 Advances Allow For Large amounts of Data to be Handled Aspect of Analysis Data Rich but Knowledge Poor

    9. 3-9 Discover Patterns Improve Business Performance Exploit favorable patterns Avoid problematic patterns Increase Understanding Predict Outcome

    10. 3-10 Answer the Key Business Questions Who will buy? What will they buy? How much? Classification and Prediction What are the different types of Customers? Segmentation of Customers

    11. 3-11 Answer the Key Business Questions What relationship exists between customers or Website visitors and the products? Association What are the groupings hidden in the data? Clustering Analysis

    12. 3-12 Data Mining Definition Non Trivial Extraction of implicit, previously unknown, interesting, and potentially useful information from data

    13. 3-13 Different Types of Data Mining Business Data Mining Scientific Data Mining Internet Data Mining

    14. 3-14 Data Mining Applications Medical Control Theory Engineering Public Administration Marketing and Finance Data Mining on the Web Scientific Data Base Fraud Detection

    15. 3-15 Data Mining Primitives Fundamental Elements Needed to Define a Data Mining Task Eight Elements (P,D,K,B,T,M,I,U) 8 - Tuple

    16. 3-16 Elements P - Problem Specification D - Task Relevant Data K - Kind of Knowledge to be Mined B - Background Knowledge T - Specific algorithms or techniques M - Models developed or knowledge patterns extracted I - Interestingness U- User

    17. 3-17 Diagram

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