Description

Book Synopsis
An introduction to the practical application of cluster analysis, Finding Groups in Data presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability.

Table of Contents
1. Introduction.

2. Partitioning Around Medoids (Program PAM).

3. Clustering large Applications (Program CLARA).

4. Fuzzy Analysis.

5. Agglomerative Nesting (Program AGNES).

6. Divisive Analysis (Program DIANA).

7. Monothetic Analysis (Program MONA).

Appendix 1. Implementation and Structure of the Programs.

Appendix 2. Running the Programs.

Appendix 3. Adapting the Programs to Your Needs.

Appendix 4. The Program CLUSPLOT.

References.

Author Index.

Subject Index.

Finding Groups in Data An Introduction to Cluster

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    A Paperback / softback by Leonard Kaufman, Peter J. Rousseeuw

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      View other formats and editions of Finding Groups in Data An Introduction to Cluster by Leonard Kaufman

      Publisher: John Wiley & Sons Inc
      Publication Date: 03/05/2005
      ISBN13: 9780471735786, 978-0471735786
      ISBN10: 0471735787
      Also in:
      Mathematics

      Description

      Book Synopsis
      An introduction to the practical application of cluster analysis, Finding Groups in Data presents a selection of methods that together can deal with most applications. These methods are chosen for their robustness, consistency, and general applicability.

      Table of Contents
      1. Introduction.

      2. Partitioning Around Medoids (Program PAM).

      3. Clustering large Applications (Program CLARA).

      4. Fuzzy Analysis.

      5. Agglomerative Nesting (Program AGNES).

      6. Divisive Analysis (Program DIANA).

      7. Monothetic Analysis (Program MONA).

      Appendix 1. Implementation and Structure of the Programs.

      Appendix 2. Running the Programs.

      Appendix 3. Adapting the Programs to Your Needs.

      Appendix 4. The Program CLUSPLOT.

      References.

      Author Index.

      Subject Index.

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