- Full Description
This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM and KD). Its chapters combine many theoretical foundations for various DM and KD methods, and they present a rich array of examples – many of which are drawn from real-life applications. Most of the theoretical developments discussed are accompanied by an extensive empirical analysis, which should give the reader both a deep theoretical and practical insight into the subjects covered. The book presents the combined research experiences of its 40 authors gathered during a long search in gleaning new knowledge from data. The last page of each chapter has a brief biographical statement of its contributors, who are world-renowned experts.
- Table of Contents
Table of Contents
- From the contents 1. A Common Logic Approach to Data Mining and Pattern Recognition (Zakrevskij).
- 2. The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery (Triantaphyllou).
- 3. An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process (Naidenova).
- 4. Discovering Rules That Govern Monotone Phenomena (Torvik, Triantaphyllou).
- 5. Learning Logic Formulas and Related Error Distributions (Felici, Sun, Truemper).
- 6. Feature Selection for Data Mining (de Angelis, Felici, Mancinelli).
- 7. Transformation of Rational and Set Data to Logic Data (Bartnikowski, Granberry, Mugan, Truemper).
- 8. Data Farming: Concepts and Methods (Kusiak).
- 9. Rule Induction Through Discrete Support Vector Decision Trees (Orsenigo, Vercellis).
- 10. Multi
- Attribute Decision Trees and Decision Rules (Lee, Olafsson).
- 11. Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective (Zhai, Khoo, Fok).
- 12. Discovering Knowledge Nuggets with a Genetic Algorithm (Noda, Freitas).
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