Soft Computing for Knowledge Discovery and Data Mining

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.

Soft Computing for Knowledge Discovery and Data Mining was written to provide investigators in the fields of information systems, engineering, computer science, statistics and management with a profound source for the role of soft computing in data mining. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. The book is also suitable for advanced-level students in computer science.

Illustrations of various applications including manufacturing, medical, banking, insurance and others Includes various real-world case studies with detailed results Edited by the highly-regarded editors of the "Data Mining and Knowledge Discovery Handbook" (2005, Springer)

Inhalt
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.