Front Cover -- Java Data Mining: Strategy, Standard, and Practice -- Copyright Page -- Contents -- Preface -- Guide to Readers -- Part I : Strategy -- Chapter 1. Overview of Data Mining -- 1.1 Why Data Mining Is Relevant Today? -- 1.2 Introducing Data Mining -- 1.3 The Value of Data Mining -- 1.4 Summary -- References -- Chapter 2. Solving Problems in Industry -- 2.1 Cross-Industry Data Mining Solutions -- 2.2 Data Mining in Industries -- 2.3 Summary -- References -- Chapter 3. Data Mining Process -- 3.1 A Standardized Data Mining Process -- 3.2 A More Detailed View of Data Analysis and Preparation -- 3.3 Data Mining Modeling, Analysis, and Scoring Processes -- 3.4 The Role of Databases and Data Warehouses in Data Mining -- 3.5 Data Mining in Enterprise Software Architectures -- 3.6 Advances in Automated Data Mining -- 3.7 Summary -- References -- Chapter 4. Mining Functions and Algorithms -- 4.1 Data Mining Functions -- 4.2 Classification -- 4.3 Regression -- 4.4 Attribute Importance -- 4.5 Association -- 4.6 Clustering -- 4.7 Summary -- References -- Chapter 5. JDM Strategy -- 5.1 What Is the JDM Strategy? -- 5.2 Role of Standards -- 5.3 Summary -- References -- Chapter 6. Getting Started -- 6.1 Business Understanding -- 6.2 Data Understanding -- 6.3 Data Preparation -- 6.4 Modeling -- 6.5 Evaluation -- 6.6 Deployment -- 6.7 Summary -- References -- Part II : Standards -- Chapter 7. Java Data Mining Concepts -- 7.1 Classification Problem -- 7.2 Regression Problem -- 7.3 Attribute Importance -- 7.4 Association Rules Problem -- 7.5 Clustering Problem -- 7.6 Summary -- References -- Chapter 8. Design of the JDM API -- 8.1 Object Modeling of Data Mining Concepts -- 8.2 Modular Packages -- 8.3 Connection Architecture -- 8.4 Object Factories -- 8.5 Uniform Resource Identifiers for Datasets -- 8.6 Enumerated Types -- 8.7 Exceptions.

8.8 Discovering DME Capabilities -- 8.9 Summary -- References -- Chapter 9. Using the JDM API -- 9.1 Connection Interfaces -- 9.2 Using JDM Enumerations -- 9.3 Using Data Specification Interfaces -- 9.4 Using Classification Interfaces -- 9.5 Using Regression Interfaces -- 9.6 Using Attribute Importance Interfaces -- 9.7 Using Association Interfaces -- 9.8 Using Clustering Interfaces -- 9.9 Summary -- References -- Chapter 10. XML Schema -- 10.1 Overview -- 10.2 Schema Elements -- 10.3 Schema Types -- 10.4 Using PMML with the JDM Schema -- 10.5 Use Cases for JDM XML Schema and Documents -- 10.6 Summary -- References -- Chapter 11. Web Services -- 11.1 What is a Web Service? -- 11.2 Service-Oriented Architecture -- 11.3 JDM Web Service -- 11.4 Enabling JDM Web Services Using JAX-RPC -- 11.5 Summary -- References -- Part III : Practice -- Chapter 12. Practical Problem Solving -- 12.1 Business Scenario 1: Targeted Marketing Campaign -- 12.2 Business Scenario 2: Understanding Key Factors -- 12.3 Business Scenario 3: Using Customer Segmentation -- 12.4 Summary -- References -- Chapter 13. Building Data Mining Tools Using JDM -- 13.1 Data Mining Tools -- 13.2 Administrative Console -- 13.3 User Interface to Build and Save a Model -- 13.4 User Interface to Test Model Quality -- 13.5 Summary -- Chapter 14. Getting Started with JDM Web Services -- 14.1 A Web Service Client in PhP -- 14.2 A Web Service Client in Java -- 14.3 Summary -- References -- Chapter 15. Impacts on IT Infrastructure -- 15.1 What Does Data Mining Require from IT? -- 15.2 Impacts on Computing Hardware -- 15.3 Impacts on Data Storage Hardware -- 15.4 Data Access -- 15.5 Backup and Recovery -- 15.6 Scheduling -- 15.7 Workflow -- 15.8 Summary -- References -- Chapter 16. Vendor Implementations -- 16.1 Oracle Data Mining -- 16.2 KXEN (Knowledge Extraction Engines).

16.3 Guidelines for New Implementers -- 16.4 Process for New JDM Users -- 16.5 Summary -- References -- Part IV : Wrapping Up -- Chapter 17. Evolution of Data Mining Standards -- 17.1 Data Mining Standards -- 17.2 Java Community Process -- 17.3 Why So Many Standards? -- 17.4 Directions for Data Mining Standards -- 17.5 Summary -- References -- Chapter 18. Preview of Java Data Mining 2.0 -- 18.1 Transformations -- 18.2 Time Series -- 18.3 Apply for Association -- 18.4 Feature Extraction -- 18.5 Statistics -- 18.6 Multi-target Models -- 18.7 Text Mining -- 18.8 Summary -- References -- Chapter 19. Summary -- Further Reading -- Glossary -- Index -- About the Authors.