Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful and insightful patterns. It is extremely important because it enables modeling and knowledge extraction from abundant data availability. Soft Computing for Knowledge Discovery and Data Mining introduces theoretical approaches and practical computing methods extending the envelope of problems that data mining can solve efficiently. From the editors of the leading Data Mining and Knowledge Discovery Handbook, 2005, this volume, by highly regarded authors, includes selected contributors of the Handbook.

This book introduces soft computing methods that extend the envelope of problems that data mining can efficiently solve. It presents practical soft-computing approaches in data mining, including various real-world case studies with detailed results and featuring illustrations of such applications as manufacturing, medical, banking, insurance and others.

Neural Network Methods.- to Soft Computing for Knowledge Discovery and Data Mining.- Neural Networks For Data Mining.- Improved SOM Labeling Methodology for Data Mining Applications.- Evolutionary Methods.- A Review of evolutionary Algorithms for Data Mining.- Genetic Clustering for Data Mining.- Discovering New Rule Induction Algorithms with Grammar-based Genetic Programming.- evolutionary Design of Code-matrices for Multiclass Problems.- Fuzzy Logic Methods.- The Role of Fuzzy Sets in Data Mining.- Support Vector Machines and Fuzzy Systems.- KDD in Marketing with Genetic Fuzzy Systems.- Knowledge Discovery in a Framework for Modelling with Words.- Advanced Soft Computing Methods and Areas.- Swarm Intelligence Algorithms for Data Clustering.- A Diffusion Framework for Dimensionality Reduction.- Data Mining and Agent Technology: a fruitful symbiosis.- Approximate Frequent Itemset Mining In the Presence of Random Noise.- The Impact of Overfitting and Overgeneralization on the Classification Accuracy in Data Mining.