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Abstract
Today data mining is no longer thought of as a set of stand-alone techniques, far from the business applications, and used only by data mining specialists or statisticians. Integrating data mining with mainstream applications is becoming an important issue for e-business applications. To support this move to applications, data mining is now an extension of the relational databases that database administrators or IT developers use. They use data mining as they would use any other standard relational function that they manipulate.
This IBM Redbook positions the new DB2 data mining functions:
- IBM DB2 Intelligent Miner Modeling (IM Modeling in this redbook)
- IBM DB2 Intelligent Miner Scoring (IM Scoring in this redbook)
- IBM DB2 Intelligent Miner Visualization (IM Visualization in this redbook)
Part 1 of this redbook helps business analysts and implementers to understand and position these new DB2 data mining functions. Part 2 provides examples for implementers on how to easily and quickly integrate the data mining functions in business applications to enhance them. And part 3 helps database administrators and IT developers to configure these functions once to prepare them for use and integration in any application.
Table of contents
Part 1. Advanced data mining functions overview
Chapter 1. Data mining functions in the database
Chapter 2. Overview of the new data mining functions
Chapter 3. Business scenario deployment examples
Part 2. Deploying data mining functions
Chapter 4. Customer profiling example
Chapter 5. Fraud detection example
Chapter 6. Campaign management solution examples
Chapter 7. Up-to-date promotion example
Chapter 8. Other possibilities of integration
Part 3. Configuring the DB2 functions for data mining
Chapter 9. IM Scoring functions for existing mining models
Chapter 10. Building the mining models using IM Modeling functions
Chapter 11. Using IM Visualization functions
Part 4. Appendixes
Appendix A. SQL script to configure database for data mining function
Appendix B. SQL scripts for the customer profiling scenario
Appendix C. SQL scripts for the fraud detection scenario
Appendix D. SQL scripts for the retention campaign scenario
Appendix E. SQL scripts for the up-to-date promotion scenario
Appendix F. UDF to create data mining models
Appendix G. UDF to extract rules from a model to a table
Appendix H. Embedding an IM Visualization applet
Appendix I. IM Scoring Java Bean code example
Appendix J. Demographic clustering: Technical differences
Appendix K. Additional material