Data Mining
Practical Machine Learning Tools and Techniques
Résumé
concepts combined with practical advice on applying machine learning tools and techniques in real-word data
mining situations. Clearly written and effectively illustrated, this book is ideal for anyone involved at any level in the
work of extracting usable knowledge from large collections of data. Complementing the book's instruction is fully
functional machine learning software. Applied to the sample data sets, these tools teach sound data mining skills;
applied to the user's own data, they are capable of discerning meaningful patterns and generating valuable insights.
The supplied Java classes can be adapted for other, more specialized machine learning schemes.
Key Features:
- For readers at all levels, offers thorough coverage of
inputs, outputs, evaluation, and basic algorithmic
methods
- Provides more technical readers with instruction on
implementation, input and output engineering, and
developing machine learning schemes in Java
- Focuses on techniques designed to generate accurate
predictions and discover comprehensible relationships
among factors-insights that can be applied in future instances
- Comes with a CD containing Java-based implementations
of various machine learning schemes-some
designed primarily for experimentation, others for real-world application
Table of contents
Preface
Acknowledgements
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
References
Index
L'auteur - Ian H. Witten
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.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Morgan Kaufmann |
| Auteur(s) | Ian H. Witten |
| Parution | 10/10/1999 |
| Nb. de pages | 368 |
| Format | 18,5 x 23 |
| Poids | 650g |
| EAN13 | 9781558605527 |
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