2010

Data Mining and Knowledge Discovery Handbook

Editors:

ISBN: 978-0-387-09822-7 (Print) 978-0-387-09823-4 (Online)

Table of contents (66 chapters)

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  1. Front Matter

    Pages i-xx

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    Book Chapter

    Pages 1-15

    Introduction to Knowledge Discovery and Data Mining

  3. Preprocessing Methods

    1. Front Matter

      Pages 17-17

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      Book Chapter

      Pages 19-32

      Data Cleansing: A Prelude to Knowledge Discovery

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      Book Chapter

      Pages 33-51

      Handling Missing Attribute Values

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      Book Chapter

      Pages 53-82

      Geometric Methods for Feature Extraction and Dimensional Reduction - A Guided Tour

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      Book Chapter

      Pages 83-100

      Dimension Reduction and Feature Selection

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      Book Chapter

      Pages 101-116

      Discretization Methods

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      Book Chapter

      Pages 117-130

      Outlier Detection

  4. Supervised Methods

    1. Front Matter

      Pages 131-131

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      Book Chapter

      Pages 133-147

      Supervised Learning

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      Book Chapter

      Pages 149-174

      Classification Trees

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      Book Chapter

      Pages 175-208

      Bayesian Networks

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      Book Chapter

      Pages 209-230

      Data Mining within a Regression Framework

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      Book Chapter

      Pages 231-247

      Support Vector Machines

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      Book Chapter

      Pages 249-265

      Rule Induction

  5. Unsupervised Methods

    1. Front Matter

      Pages 267-267

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      Book Chapter

      Pages 269-298

      A survey of Clustering Algorithms

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      Book Chapter

      Pages 299-319

      Association Rules

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      Book Chapter

      Pages 321-338

      Frequent Set Mining

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      Book Chapter

      Pages 339-354

      Constraint-based Data Mining

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      Book Chapter

      Pages 355-368

      Link Analysis

  6. Soft Computing Methods

    1. Front Matter

      Pages 369-369

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      Book Chapter

      Pages 371-400

      A Review of Evolutionary Algorithms for Data Mining

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      Book Chapter

      Pages 401-417

      A Review of Reinforcement Learning Methods

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