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Data Mining and Knowledge Discovery Handbook

  • Book
  • © 2005

Overview

Editors:
  1. Oded Maimon

    1. Dept. of Industrial Engineering, Tel-Aviv University, Ramat-Aviv, Israel

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  2. Lior Rokach

    1. Dept. of Industrial Engineering, Tel-Aviv University, Ramat-Aviv, Israel

    View editor publications

    You can also search for this editor in PubMed   Google Scholar

  • Most complete, extensive and modern handbook available today in the field of data mining, the core of the knowledge discovery process
  • Algorithmic descriptions are detailed so the reader can understand exactly how they work, and thus implement, modify and intelligently use them
  • Includes detailed tutorials, and each topic is supplemented with references for further study
  • Includes supplementary material: sn.pub/extras

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Table of contents (68 chapters)

  1. Front Matter

    Pages i-xxxv
    Download chapter PDF

  2. Introduction to Knowledge Discovery in Databases

    1. Introduction to Knowledge Discovery in Databases

      • Oded Maimon, Lior Rokach
      Pages 1-17

  3. Preprocessing Methods

    1. Data Cleansing

      • Jonathan I. Maletic, Andrian Marcus
      Pages 21-36

    2. Handling Missing Attribute Values

      • Jerzy W. Grzymala-Busse, Witold J. Grzymala-Busse
      Pages 37-57

    3. Geometric Methods for Feature Extraction and Dimensional Reduction

      • Christopher J. C. Burges
      Pages 59-91

    4. Dimension Reduction and Feature Selection

      • Barak Chizi, Oded Maimon
      Pages 93-111

    5. Discretization Methods

      • Ying Yang, Geoffrey I. Webb, Xindong Wu
      Pages 113-130

    6. Outlier Detection

      • Irad Ben-Gal
      Pages 131-146

  4. Supervised Methods

    1. Introduction to Supervised Methods

      • Oded Maimon, Lior Rokach
      Pages 149-164

    2. Decision Trees

      • Lior Rokach, Oded Maimon
      Pages 165-192

    3. Bayesian Networks

      • Paola Sebastiani, Maria M. Abad, Marco F. Ramoni
      Pages 193-230

    4. Data Mining within a Regression Framework

      • Richard A. Berk
      Pages 231-255

    5. Support Vector Machines

      • Armin Shmilovici
      Pages 257-276

    6. Rule Induction

      • Jerzy W. Grzymala-Busse
      Pages 277-294

  5. Unsupervised Methods

    1. Visualization and Data Mining for High Dimensional Datasets

      • Alfred Inselberg
      Pages 297-319

    2. Clustering Methods

      • Lior Rokach, Oded Maimon
      Pages 321-352

    3. Association Rules

      • Frank Höppner
      Pages 353-376

    4. Frequent Set Mining

      • Bart Goethals
      Pages 377-397

    5. Constraint-Based Data Mining

      • Jean-Francois Boulicaut, Baptiste Jeudy
      Pages 399-416

    6. Link Analysis

      • Steve Donoho
      Pages 417-432
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Keywords

About this book

Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository.

This book 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 is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.

Editors and Affiliations

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

    Oded Maimon, Lior Rokach

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