Knowledge Discovery and Data Mining: The Info-Fuzzy Network (IFN) Methodology / Edition 1
by O. Maimon, M. Last
This book presents a unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network. The IFN methodology handles a selection of the most relevant features, extraction of informative rules and patterns, and post-processing of the extracted knowledge. This book provides detailed descriptions of the IFN algorithms and discusses
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This book presents a unified approach to Knowledge Discovery and Data Mining, termed IFN for Information Fuzzy Network. The IFN methodology handles a selection of the most relevant features, extraction of informative rules and patterns, and post-processing of the extracted knowledge. This book provides detailed descriptions of the IFN algorithms and discusses real-world case studies from several application domains including manufacturing, process engineering, health care, and education. In addition, the book describes the methodology of applications and compares the IFN performance to other data mining methods.
Audience: This book is intended to be used by researchers in the field of information systems, engineering, computer science, statistics, and management who are searching for a unified theoretical approach to the knowledge discovery process. The book can also serve as a reference book for courses on data mining, machine learning, and databases.
Product Details
- ISBN-13:
- 9780792366478
- Publisher:
- Springer US
- Publication date:
- 12/31/2000
- Series:
- Massive Computing Series, #1
- Edition description:
- 2001
- Pages:
- 168
- Product dimensions:
- 9.21(w) x 6.14(h) x 0.50(d)
Table of Contents
List of Figures. List of Tables. Acknowledgements. Preface. Part I: Information-Theoretic Approach to Knowledge Discovery. 1. Introduction. 2. Automated data pre-processing. 3. Information-Theoretic Connectionist Networks. 4. Post-Processing of Data Mining Results. Part II: Application Methodology and Case Studies. 5. Methodology of Application. 6. Case Studies. Part III: Comparative Study and Advanced Issues. 7. Comparative Study. 8. Advanced Data Mining Methods. 9. Summary and Some Open Problems. References. Appendices. Index.
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