Java Data Mining: Strategy, Standard, and Practice

A Practical Guide for architecture, design, and implementation

Java Data Mining: Strategy, Standard, and Practice on ScienceDirect(Opens new window)
Paperback, 544 Pages
Published: NOV-2006
ISBN 10: 0-12-370452-9
ISBN 13: 978-0-12-370452-8
Imprint: MORGAN KAUFMANN


By
Mark Hornick, Sr. Manager, Data Mining Technologies, Oracle Corporation, Burlington, MA
Erik Marcadé, Founder and Chief Technical Officer, KXEN, Paris, France
Sunil Venkayala, Principal Member of Technical Staff, Oracle, Burlington, MA

Description
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. The book discusses and illustrates how to solve real problems using the JDM API. The authors provide you with: * 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

Audience:
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.


 
Last update: 5 Nov 2011