This book integrates two areas of computer science namely data mining andevolutionary algorithms. Both these areas have become increasingly popular inthe last f... » 

This book integrates two areas of computer science namely data mining andevolutionary algorithms. Both these areas have become increasingly popular inthe last few years and their integration is currently an area of activeresearch.In general data mining consists of extracting knowledge from data. Inthis book we particularly emphasize the importance of discoveringcomprehensible interesting knowledge which is potentially useful for thereader for intelligent decision making.In a nutshell the motivation forapplying evolutionary algorithms to data mining is that evolutionary algorithmsare robust search methods which perform a global search in the space ofcandidate solutions. In contrast most rule induction methods perform a localgreedy search in the space of candidate rules. Intuitively the global searchof evolutionary algorithms can discover interesting rules and patterns thatwould be missed by the greedy search. TOCPreface 1. Introduction 2. DataMining Tasks and Concepts 3. Data Mining Paradigms 4. Data Prepration 5.Basic Concepts of Evolutionary Algorithms 6. Genetic Algorithms for RuleDiscovery 7. Genetic Programming for Rule Discovery and DecisionTreeBuilding 8. Evolutionary Algorithms for Clustering 9. Evolutionary Algorithmsfor Data Preparation 10. Evolutionary Algorithms for Discovering Fuzzy Rules11. Scaling up Evolutionary Algorithms for Large Data Sets 12. Conclusions andResearch Directions Index. «