Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

✍ Scribed by Arkadij D. Zakrevskij (auth.), Evangelos Triantaphyllou, Giovanni Felici (eds.)

• Publisher: Springer US
• Year: 2006
• Tongue: English
• Leaves: 775
• Series: Massive Computing 6
• Edition: 1
• Category: Library

✦ Synopsis

This book will give the reader a perspective into the core theory and practice of data mining and knowledge discovery (DM&KD). Its chapters combine many theoretical foundations for various DM&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.

Audience

The intended audience for this book includes graduate students studying data mining who have some background in mathematical logic and discrete optimization, as well as researchers and practitioners in the same area.

✦ Table of Contents

A Common Logic Approach to Data Mining and Pattern Recognition....Pages 1-43
The One Clause at a Time (OCAT) Approach to Data Mining and Knowledge Discovery....Pages 45-87
An Incremental Learning Algorithm for Inferring Logical Rules from Examples in the Framework of the Common Reasoning Process....Pages 89-147
Discovering Rules That Govern Monotone Phenomena....Pages 149-192
Learning Logic Formulas and Related Error Distributions....Pages 193-226
Feature Selection for Data Mining....Pages 227-252
Transformation of Rational Data and Set Data to Logic Data....Pages 253-278
Data Farming: Concepts and Methods....Pages 279-304
Rule Induction Through Discrete Support Vector Decision Trees....Pages 305-326
Multi-Attribute Decision Trees and Decision Rules....Pages 327-358
Knowledge Acquisition and Uncertainty in Fault Diagnosis: A Rough Sets Perspective....Pages 359-394
Discovering Knowledge Nuggets with a Genetic Algorithm....Pages 395-432
Diversity Mechanisms in Pitt-Style Evolutionary Classifier Systems....Pages 433-457
Fuzzy Logic in Discovering Association Rules: An Overview....Pages 459-493
Mining Human Interpretable Knowledge with Fuzzy Modeling Methods: An Overview....Pages 495-550
Data Mining from Multimedia Patient Records....Pages 551-595
Learning to Find Context Based Spelling Errors....Pages 597-627
Induction and Inference with Fuzzy Rules for Textual Information Retrieval....Pages 629-653
Statistical Rule Induction in the Presence of Prior Information: The Bayesian Record Linkage Problem....Pages 655-694
Some Future Trends in Data Mining....Pages 695-716

✦ Subjects

Information Storage and Retrieval; Operations Research, Mathematical Programming; Operations Research/Decision Theory