Data Mining: Practical Machine Learning Tools and Techniques with Java Implement
暫譯: 資料探勘：使用 Java 實作的實用機器學習工具與技術

"This is a milestone in the synthesis of data mining, data analysis, information theory, and machine learning."

-Jim Gray, Microsoft Research

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, youll learn all you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data miningincluding both tried-and-true techniques of the past and Java-based methods at the leading edge of contemporary research. If youre 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.

Authors:

Ian H. Witten is professor of computer science at the University of Waikato in New Zealand. He is a fellow of the ACM and the Royal Society of New Zealand and a member of professional computing, information retrieval, and engineering associations in the UK, US, Canada, and New Zealand. He is coauthor of Managing Gigabytes (1999), The Reactive Keyboard (1992), and Text Compression (1990) and author of many journal articles and conference papers.

Eibe Frank is a researcher in the Machine Learning group at the University of Waikato. He holds a degree in computer science from the University of Karlsruhe in Germany and is the author of several papers, both presented at machine learning conferences and published in machine learning journals.

Table of Contents:

1. Whats It All About?
2. Input: Concepts, Instances, Attributes
3. Output: Knowledge Representation
4. Algorithms: The Basic Methods
5. Credibility: Evaluating Whats 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

Web-Enhanced:

Check out the accompanying software at the author's site: http://www.cs.waikato.ac.nz/ml/weka

Teaching material

• Powerpoint slides
  • A Powerpoint presentation containing all the figures from the book can be downloaded by clicking here.
• PDF slides
  • Part I
  • Part II
  • Part III
  • Part IV
  • Part V
  • Part VI
  • Part VII
• Exams
  • An example exam.
  • An exam and the corresponding answers. [Available to instructors only; request a password from your academic sales representative]
• Assignments
  • Assignment 1
  • Assignment 2
  • Assignment 3
  • Assignment 4
  • Assignment 5
  • Assignment 6
• Quizzes
  • Quiz 1
  • Quiz 2
  • Quiz 3
  • Quiz 4
  • Quiz 5
  • Quiz 6
  • Quiz 7
  • Quiz 8
  • Quiz 9

Related Titles:

Database
Artificial Intelligence