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How Many Clusters? An Investigation of Five Procedures for Detecting Nested Cluster Structure

  • A. D. Gordon
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Summary

The paper addresses the problem of identifying relevant values for the number of clusters present in a data set. The problem has usually been tackled by searching for a best partition using so-called stopping rules. It is argued that it can be of interest to detect cluster structure at several different levels, and five stopping rules that performed well in a previous investigation are modified for this purpose. The rules are assessed by their performance in the analysis of simulated data sets which contain nested cluster structure.

Keywords

Cluster Structure Single Link Local Rule Cluster Criterion Global Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • A. D. Gordon
    • 1
  1. 1.Mathematical InstituteUniversity of St AndrewsNorth Haugh, St AndrewsScotland

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