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Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations Cover

 

Synopses & Reviews

Publisher Comments:

This book offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. Inside, you'll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining—including both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If you're involved at any level in the work of extracting usable knowledge from large collections of data, this clearly written and effectively illustrated book will prove an invaluable resource.

Complementing the authors' instruction is a fully functional platform-independent Java software system for machine learning, available for download. Apply it to the sample data sets provided to refine your data mining skills, apply it to your own data to discern meaningful patterns and generate valuable insights, adapt it for your specialized data mining applications, or use it to develop your own machine learning schemes.

* Helps you select appropriate approaches to particular problems and to compare and evaluate the results of different techniques.

* Covers performance improvement techniques, including input preprocessing and combining output from different methods.

* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.

Synopsis:

se it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.

Synopsis:

ding input preprocessing and combining output from different methods.

* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.

About the Author

<>Ian H. Witten is a professor of computer science at the University of Waikato in New Zealand. He directs the New Zealand Digital Library research project. His research interests include information retrieval, machine learning, text compression, and programming by demonstration. He received an MA in Mathematics from Cambridge University, England; an MSc in Computer Science from the University of Calgary, Canada; and a PhD in Electrical Engineering from Essex University, England. He is a fellow of the ACM and of the Royal Society of New Zealand. He has published widely on digital libraries, machine learning, text compression, hypertext, speech synthesis and signal processing, and computer typography. He has written several books, the latest being Managing Gigabytes (1999) and Data Mining (2000), both from Morgan Kaufmann.Eibe Frank lives in New Zealand with his Samoan spouse and two lovely boys, but originally hails from Germany, where he received his first degree in computer science from the University of Karlsruhe. He moved to New Zealand to pursue his Ph.D. in machine learning under the supervision of Ian H. Witten, and joined the Department of Computer Science at the University of Waikato as a lecturer on completion of his studies. He is now an associate professor at the same institution. As an early adopter of the Java programming language, he laid the groundwork for the Weka software described in this book. He has contributed a number of publications on machine learning and data mining to the literature and has refereed for many conferences and journals in these areas.>

University of Waikato, Hamilton, New Zealand. Recipient of the 2005 ACM SIGKDD Service Award.

Table of Contents

1. What's It All About?

2. Input: Concepts, Instances, Attributes

3. Output: Knowledge Representation

4. Algorithms: The Basic Methods

5. Credibility: Evaluating What's Been Learned

6. Implementations: Real Machine Learning Schemes

7. Moving On: Engineering The Input And Output

8. Nuts And Bolts: Machine Learning Algorithms In Java

9. Looking Forward

Product Details

ISBN:
9781558605527
Subtitle:
Practical Machine Learning Tools and Techniques with Java Implementations
Foreword:
Gray, Jim
Author:
Frank, Witten, Ian, Eibe
Author:
Frank, Witten, Ian H., Eibe
Author:
Gray, Jim
Author:
Witten, I. H.
Publisher:
Morgan Kaufmann
Location:
San Francisco, Calif. :
Subject:
Artificial Intelligence
Subject:
Database Management - Data Warehousing and Data Mining
Subject:
Data mining
Subject:
Java (Computer program language)
Subject:
Programming Languages - Java
Subject:
Java
Subject:
Database Management - Database Mining
Subject:
Artificial Intelligence - General
Subject:
Information Storage & Retrieval
Subject:
Database Management - General
Series:
Morgan Kaufmann Series in Data Management Systems
Series Volume:
no. 10
Publication Date:
19991011
Binding:
Paperback
Language:
English
Pages:
371
Dimensions:
9.12x7.29x.82 in. 1.45 lbs.

Related Subjects

Computers and Internet » Artificial Intelligence » General
Computers and Internet » Database » Design

Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations
0 stars - 0 reviews
$ In Stock
Product details 371 pages Morgan Kaufmann - English 9781558605527 Reviews:
"Synopsis" by , se it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.
"Synopsis" by , ding input preprocessing and combining output from different methods.

* Comes with downloadable machine learning software: use it to master the techniques covered inside, apply it to your own projects, and/or customize it to meet special needs.

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