Data Mining : Practical Machine Learning Tools and Techniques

Data Mining : Practical Machine Learning Tools and Techniques

ABOUT THIS BOOK

This book presents this new discipline in a very accessible form:as a text both to train the next generation ofpractitioners and researchers and to inform lifelong learners like myself.Witten and Frank have a passion for simple and elegant solutions.They approach each topic with this mindset,grounding all concepts in concrete examples,and urging the reader to consider the simple techniques first,and then progress to the more sophisticated ones ifthe simple ones prove inadequate.
Ifyou are interested in databases,and have not been following the machine learning field,this book is a great way to catch up on this exciting progress.If you have data that you want to analyze and understand,this book and the associated Weka toolkit are an excellent way to start

CONTENTS

Part I  Machine learning tools and techniques
1. What’s it all about?

2. Input: Concepts, instances, and attributes

3. Output: Knowledge representation

4. Algorithms: The basic methods

5. Credibility: Evaluating what’s been learned

6. Implementations: Real machine learning schemes

7. Transformations: Engineering the input and output

8. Moving on: Extensions and applications

Part II  The Weka machine learning workbench

9. Introduction to Weka

10. The Explorer

11. The Knowledge Flow interface
12. The Experimenter

13. The command-line interface

14. Embedded machine learning

15. Writing new learning schemes

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