Java Data Mining: Strategy, Standard, and Practice,
Edition 1 A Practical Guide for Architecture, Design, and Implementation
By Mark F. Hornick, Erik Marcadé and Sunil Venkayala

Publication Date: 07 Nov 2006
Whether you are a software developer, systems architect, data analyst, or business analyst, if you want to take advantage of data mining in the development of advanced analytic applications, Java Data Mining, JDM, the new standard now implemented in core DBMS and data mining/analysis software, is a key solution component. This book is the essential guide to the usage of the JDM standard interface, written by contributors to the JDM standard.

Key Features

  • Data mining introduction - an overview of data mining and the problems it can address across industries; JDM's place in strategic solutions to data mining-related problems
  • JDM essentials - concepts, design approach and design issues, with detailed code examples in Java; a Web Services interface to enable JDM functionality in an SOA environment; and illustration of JDM XML Schema for JDM objects
  • JDM in practice - the use of JDM from vendor implementations and approaches to customer applications, integration, and usage; impact of data mining on IT infrastructure; a how-to guide for building applications that use the JDM API
  • Free, downloadable KJDM source code referenced in the book available here
About the author
By Mark F. Hornick, Sr. Manager, Data Mining Technologies, Oracle Corporation, Burlington, MA; Erik Marcadé, Founder and Chief Technical Officer, KXEN, Paris, France and Sunil Venkayala, Principal Member of Technical Staff, Oracle, Burlington, MA
Table of Contents
Guide to Readers

Part I - Strategy
1. Overview of Data Mining
1.1. Why is data mining relevant today?
1.2. Introducing Data Mining
1.3. The Value of Data Mining
1.4. Summary
1.5. References

2. Solving Problems in Industry
2.1. Cross-industry data mining solutions
2.2. Data Mining in Industries
2.3. Summary
2.4. References

3. Data Mining Process
3.1. A standardized data mining process
3.2. Data Analysis and Preparation…a more detailed view
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
3.8. References

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
4.8. References

5. JDM Strategy
5.1. What is the JDM strategy?
5.2. Role of Standards
5.3. Summary
5.4. References

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
6.8. References

Part II - Standard
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
7.7. References

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. URI for Datasets
8.6. Enumerated Types
8.7. Exceptions
8.8. Discovering DME Capabilities
8.9. Summary
8.10. References

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
9.10. References

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
10.7. References

11. Web Services
11.1. What is a Web Service?
11.2. Service Oriented Architecture (SOA)
11.3. JDM Web Service (JDMWS)
11.4. Enabling JDM Web Services using JAX-RPC
11.5. Summary
11.6. References

Part III - Practice
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
12.5. Bibliography

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

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
14.4. References

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
15.9. References

16. Vendor implementations
16.1. Oracle Data Mining
16.2. KXEN (Knowledge eXtraction ENgines)
16.3. Process for new Vendors
16.4. Process for new JDM users
16.5. Summary
16.6. References

Part IV. Wrapping Up
17. Evolution of Data Mining Standards
17.1. Data Mining Standards
17.2. Java Community Process
17.3. Why so many standards?
17.4. Where data mining standards have been and where will they go?
17.5. Directions for data mining standards
17.6. Summary
17.7. References

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
18.9. References

19. Summary

App. A. Further Reading
App. B. Glossary
Book details
ISBN: 9780123704528
Page Count: 544
Illustrations : Approx. 110 illustrations
Retail Price : £46.99
This book is for software developers and applications architects interested in or who need data mining analysis as part of their application. It can be used by both novice and advanced java developers as a reference for incorporating data mining into applications, leveraging the sample code provided. For example, a Java developer may know he wants to classify a customer's interest in a product, but doesn't know how to get started. This book provides a quick start for using data mining in a practical context. On the other hand, experienced data miners who use Java will also gain benefits by seeing working code of how to use JSM to accomplish mining task