Soft Computing for Knowledge Discovery and Data Mining

Data mining is the science and technology of exploring large and complex bodies of data in order to discover useful 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 soft computing methods extending the envelope of problems that data mining can solve efficiently. It presents practical soft-computing approaches in data mining. This edited volume by highly regarded authors, includes several contributors of the 2005, Data Mining and Knowledge Discovery Handbook.  This book 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. Not only does this book feature illustrations of various applications including manufacturing, medical, banking, insurance and others, but also includes various real-world case studies with detailed results.

Soft Computing for Knowledge Discovery and Data Mining is designed for practitioners and researchers in industry. Practitioners and researchers may be particularly interested in the description of real world data mining projects performed with soft computing. This book is also suitable as a secondary textbook or reference for advanced-level students in information systems, engineering, computer science and statistics management.

• Bayesian network
• Clustering
• DOM
• Support Vector Machine
• algorithms
• classification
• data mining
• evolutionary algorithm
• fuzzy
• genetic programming
• information system
• intelligence
• knowledge discovery

• Tel Aviv University, Israel

  Oded Maimon

• Ben-Gurion University, Israel

  Lior Rokach

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