The Handbook of Data Mining [NOOK Book]

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. ...
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The Handbook of Data Mining

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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: 9781410607515
  • Publisher: Taylor & Francis, Inc.
  • Publication date: 4/1/2003
  • Sold by: Barnes & Noble
  • Format: eBook
  • Pages: 720
  • File size: 13 MB
  • Note: This product may take a few minutes to download.

Table of Contents

Foreword
Preface
About the Editor
Advisory Board
Contributors
I Methodologies of Data Mining
1 Decision Trees 3
2 Association Rules 25
3 Artificial Neural Network Models for Data Mining 41
4 Statistical Analysis of Normal and Abnormal Data 67
5 Bayesian Data Analysis 103
6 Hidden Markov Processes and Sequential Pattern Mining 133
7 Strategies and Methods for Prediction 159
8 Principal Components and Factor Analysis 193
9 Psychometric Methods of Latent Variable Modeling 215
10 Scalable Clustering 247
11 Time Series Similarities and Indexing 279
12 Nonlinear Time Series Analysis 305
13 Distributed Data Mining 341
II Management of Data Mining
14 Data Collection, Preparation, Quality, and Visualization 365
15 Data Storage and Management 393
16 Feature Extraction, Selection, and Construction 409
17 Performance Analysis and Evaluation 425
18 Security and Privacy 441
19 Emerging Standards and Interfaces 453
III Applications of Data Mining
20 Mining Human Performance Data 463
21 Mining Text Data 481
22 Mining Geospatial Data 519
23 Mining Science and Engineering Data 549
24 Mining Data in Bioinformatics 573
25 Mining Customer Relationship Management (CRM) Data 597
26 Mining Computer and Network Security Data 617
27 Mining Image Data 637
28 Mining Manufacturing Quality Data 657
Author Index 669
Subject Index 681
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