The Handbook of Data Mining / Edition 1

The Handbook of Data Mining / Edition 1

by Nong Ye
     
 

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

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Overview

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.

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Product Details

ISBN-13:
9780805840810
Publisher:
Taylor & Francis
Publication date:
04/28/2003
Series:
Human Factors and Ergonomics Series
Pages:
720
Product dimensions:
7.10(w) x 10.00(h) x 1.60(d)

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Table of Contents

Contents: G. Salvendy, Foreword. N. Ye, Preface. Part I:Methodologies of Data Mining. J. Gehrke, Decision Trees. G.I. Webb, Association Rules. J. Si, B.J. Nelson, G.C. Runger, Artificial Neural Network Models for Data Mining. C.M. Borror, Statistical Analysis of Normal and Abnormal Data. D. Madigan, G. Ridgeway, Bayesian Data Analysis. S.L. Scott, Hidden Markov Processes and Sequential Pattern Mining. G. Ridgeway, Strategies and Methods for Prediction. D.W. Apley, Principal Components and Factor Analysis. E. Ip, I. Cadez, P. Smyth, Psychometric Methods of Latent Variable Modeling. J. Ghosh, Scalable Clustering. G. Das, D. Gunopulos, Time Series Similarity and Indexing. Y-C. Lai, Z. Liu, N. Ye, T. Yalcinkaya, Nonlinear Time Series Analysis. B-H. Park, H. Kargupta, Distributed Data Mining. Part II:Management of Data Mining. D. Pyle, Data Collection, Preparation, Quality, and Visualization. T. Wu, X. Li, Data Storage and Management. H. Liu, L. Yu, H. Motoda, Feature Extraction, Selection, and Construction. S.M. Weiss, T. Zhang, Performance Analysis and Evaluation. C. Clifton, Security and Privacy. R. Grossman, M. Hornick, G. Meyer, Emerging Standards and Interfaces. Part III:Applications of Data Mining. D.A. Nembhard, Mining Human Performance Data. R. Feldman, Mining Text Data. S. Shekhar, R.R. Vatsavai, Mining Geospatial Data. C. Kamath, Mining Science and Engineering Data. M.J. Zaki, Mining Data in Bioinformatics. R. Cooley, Mining Customer Relationship Management (CRM) Data. N. Ye, Mining Computer and Network Security Data. C. Djeraba, G. Fernandez, Mining Image Data. M.C. Testik, G.C. Runger, Mining Manufacturing Quality Data.

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