Java Data Mining: Strategy, Standard, and Practice

1st Edition

A Practical Guide for Architecture, Design, and Implementation

Print ISBN: 9780123704528
eBook ISBN: 9780080495910
Imprint: Morgan Kaufmann
Published Date: 7th November 2006
Page Count: 544
49.95 + applicable tax
39.99 + applicable tax
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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


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

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

  1. Java Data Mining Concepts
    7.1. Classification problem <BR id=


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© Morgan Kaufmann 2007
Morgan Kaufmann
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"This is not only a great introduction to JDM, but also a great introduction for a practitioner to data mining in general. This is a “must-have" for anyone developing large-scale data mining applications in Java." --Robert Grossman, Open Data Group and University of Illinois at Chicago

"It pleases me that the Java Community ProcessSM(JCPSM) Program could host the development of the Data Mining standard, JSR 73, whose evolution and usability are presented so compellingly in Java Data Mining: Standard, Strategy, and Practice. The authors have taken a unique approach to describing a broad range of aspects from strategies to problem solving with data mining technology in a variety of industries. The book is a ”must-read” for those who want to introduce themselves to Java data mining (JDM) and fully realize the strategic importance of this technology in an ever competitive environment." --Onno Kluyt, senior director, JCP Program at Sun Microsystems, Inc., and chair of the JCP

"Java is now ubiquitous and over the past few years the Java world has shifted focus on--among other things--new frameworks, such as the Java Data Mining (JDM) framework. JDM addresses a clear need for standardization in data mining operations, yet to those approaching both Java and data mining the mountain seems as Everest. Hornick, Marcadé, and Venkayala could not have written this book at a better time. To the expert it is reference and map of the landscape, and to the novice it will be a constant guide and companion to each journey in JDM. This book is approachable, usable, practical, and necessary for any Java data mining software architect, developer, or analyst." --Frank Byrum, Chief Scientist, CorMine Intelligent Data, LLC