Triantaphyllou E., Felici G. (eds.) Data Mining and Knowledge Discovery Approaches Based on Rule Induction Techniques

Springer, 2006. — 648 p. — (Massive Computing Series, vol. 6). — ISBN: 978-0-387-34296-2; ISBN: 978-0-387-34294-8; ISBN: 978-1-4419-4173-2This 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.AudienceThe 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.A common logic approach to data mining and pattern recognition
The one clause at a time (ocat) approach to data mining and knowledge discovery
An incremental learning algorithm for inferring logical rules from examples in the framework of the common reasoning process
Discovering rules that govern monotone phenomena
Learning logic formulas and related error distributions
Feature selection for data mining
Transformation of rational and set data to logic data
Data farming: concepts and methods
Rule induction through discrete support vector decision trees
Multi-attribute decision trees and decision rules
Knowledge acquisition and uncertainty in fault diagnosis: a rough sets perspective
Discovering knowledge nuggets with a genetic algorithm
Diversity mechanisms in pitt-style evolutionary classifier systems
Fuzzy logic in discovering association rules: an overview,
Mining human interpretable knowledge with fuzzy modeling methods: an overview
Data mining from multimedia patient records
Learning to find context based spelling errors
Induction and inference with fuzzy rules for textual information retrieval
Statistical rule induction in the presence of prior information: the bayesian record linkage problem
Future trends in some data mining areas
Subject Index
Author Index
Contributor Index
About the Editors