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Overview
Authored by international researchers in academia and industry, 28 contributions aim to present a comprehensive treatment of the methodologies, management, and applications of data mining. Each concept or tool is illustrated by simple examples and real-world applications. A sampling of topics includes Bayesian data analysis, security issues, and performance analysis and evaluation. Some of the applications described include the mining of human performance data, geospatial data, and manufacturing quality data. Editor Ye teaches industrial engineering at Arizona State University. Annotation (c)2003 Book News, Inc., Portland, ORSynopsis
Created with the input of a distinguished International Board of the foremost authorities in data mining from academia and industry, The Handbook of Data Mining presents comprehensive coverage of data mining concepts and techniques. Algorithms, methodologies, management issues, and tools are all illustrated through engaging examples and real-world applications to ease understanding of the materials.
This book is organized into three parts. Part I presents various data mining methodologies, concepts, and available software tools for each methodology. Part II addresses various issues typically faced in the management of data mining projects and tips on how to maximize outcome utility. Part III features numerous real-world applications of these techniques in a variety of areas, including human performance, geospatial, bioinformatics, on- and off-line customer transaction activity, security-related computer audits, network traffic, text and image, and manufacturing quality.
This Handbook is ideal for researchers and developers who want to use data mining techniques to derive scientific inferences where extensive data is available in scattered reports and publications. It is also an excellent resource for graduate-level courses on data mining and decision and expert systems methodology.