Data Mining and Knowledge Discovery Handbook (eBook, PDF)

Redaktion: Maimon, Oded; Rokach, Lior

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer New York
• Seitenzahl: 1285
• Erscheinungstermin: 10. September 2010
• Englisch
• ISBN-13: 9780387098234
• Artikelnr.: 37286801

Autorenporträt

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.

Inhaltsangabe

Introduction to knowledge discovery in databases.- Part I Preprocessing methods.- Data cleansing.- Handling missing attribute values.- Geometric methods for feature extraction and dimensional reduction.- Dimension Reduction and feature selection.- Discretization methods.- outlier detection.- Part II Supervised methods.- Introduction to supervised methods.- Decision trees.- Bayesian networks.- Data mining within a regression framework.- Support vector machines.- Rule induction.- Part III Unsupervised methods.- Visualization and data mining for high dimensional datasets.- Clustering methods.- Association rules.- Frequent set mining.- Constraint-based data mining.- Link analysis.- Part IV Soft computing methods.- Evolutionary algorithms for data mining.- Reinforcement-learning: an overview from a data mining perspective.- Neural networks.- On the use of fuzzy logic in data mining.- Granular computing and rough sets.- Part V Supporting methods.- Statistical methods for data mining.- Logics for data mining.- Wavelet methods in data mining.- Fractal mining.- Interestingness measures.- Quality assessment approaches in data mining.- Data mining model comparison.- Data mining query languages.- Part VI Advanced methods.- Meta-learning.- Bias vs variance decomposition for regression and classification.- Mining with rare cases.- Mining data streams.- Mining high-dimensional data.- Text mining and information extraction.- Spatial data mining.- Data mining for imbalanced datasets: an overview.- Relational data mining.- Web mining.- A review of web document clustering approaches.- Causal discovery.- Ensemble methods for classifiers.- Decomposition methodology for knowledge discovery and data mining.- Information fusion.- Parallel and grid-based data mining.- Collaborative data mining.- Organizational data mining.- Mining time series data.- Part VII Applications.- Data mining in medicine.- Learning information patterns in biological databases.- Data mining for selection of manufacturing processes.- Data mining of design products and processes.- Data mining in telecommunications.- Data mining for financial applications.- Data mining for intrusion detection.- Data mining for software testing.- Data mining for CRM.- Data mining for target marketing.- Part VIII Software.- Oracle data mining.- Building data mining solutions with OLE DB for DM and XML for analysis.- LERS-A data mining system.- GainSmarts data mining system for marketing.- WizSoft's WizWhy.- DataEngine.- Index.

Rezensionen

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)