Data Mining and Knowledge Discovery Handbook

Knowledge Discovery demonstrates intelligent computing at its best, and is the most desirable and interesting end-product of Information Technology. To be able to discover and to extract knowledge from data is a task that many researchers and practitioners are endeavoring to accomplish. There is a lot of hidden knowledge waiting to be discovered – this is the challenge created by today’s abundance of data.

Data Mining and Knowledge Discovery Handbook, Second Edition organizes the most current concepts, theories, standards, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This handbook first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security.

Data Mining and Knowledge Discovery Handbook, Second Edition is designed for research scientists, libraries and advanced-level students in computer science and engineering as a reference. This handbook is also suitable for professionals in industry, for computing applications, information systems management, and strategic research management.

• Bayesian networks
• KAP_D018
• KDD
• KLT
• KLTcatalog
• algorithm
• currentjm
• data mining
• data mining applications
• decision trees
• ensemble method
• knowledge discovery
• large datasets
• preprocessing method
• soft computing method
• statistical method
• text min

From the reviews of the second edition:

“This handbook provides an excellent guide in every aspect of the discovery process. … Contributors are drawn from noted academic institutions and companies around the world and across diverse disciplines. … serves to define the current state of the art in knowledge discovery, and is particularly useful in cross-fertilization among a diverse set of application scenarios. It is an indispensable reference for researchers and an excellent starting point for advanced students taking graduate courses in this area. Summing Up: Highly recommended. Upper-division undergraduates through professionals/practitioners.” (J. Y. Cheung, Choice, Vol. 48 (10), June, 2011)

“This edition treats new aspects (for instance, privacy) and new methods, like those based on swarm intelligence and multi-label classification. … The book is a comprehensive and detailed reference. … Each chapter contains a long list of references for further investigation. … I recommend this comprehensive book to advanced readers--including designers and architects at software companies--interested in the R&D of data mining.” (K. Balogh, ACM Computing Reviews, November, 2011)

• , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, Israel

  Oded Maimon

• , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel

  Lior Rokach

Prof. Oded Maimon is the Oracle chaired Professor at Tel-Aviv University, Previously at MIT. Oded is a leader expert in the field of data mining and knowledge discovery. He published many articles on new algorithms and seven significant award winning books in the field since 2000. He has also developed and implemented successful applications in the Industry. He heads an international research group sponsored by European Union awards.

Dr. Lior Rokach is a senior lecturer at the Department of Information System Engineering at Ben-Gurion University. He is a recognized expert in intelligent information systems and has held several leading positions in this field. His main areas of interest are Data Mining, Pattern Recognition, and Recommender Systems. Dr. Rokach is the author of over 70 refereed papers in leading journals, conference proceedings and book chapters. In addition he has authored six books and edited three others books.

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