Clustering algorithms can provide misleading summaries of data, and attention has been devoted to investigating ways of guarding against reaching incorrect conclusions, by validating the results of a cluster analysis. The paper provides an overview of recent work in this area of cluster validation. Material covered includes: the distinction between external, internal, and relative clustering indices; types of null model, including ‘data-influenced’ null models; tests of the complete absence of any class structure in a data set; and ways of assessing the validity of individual clusters, partitions of data into disjoint clusters, and hierarchical classifications. A discussion indicates areas in which further research seems desirable.


Cluster Algorithm Null Model Random Graph Class Structure American Statistical Association 
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Copyright information

© Springer Japan 1998

Authors and Affiliations

  • A. D. Gordon
    • 1
  1. 1.Mathematical InstituteUniversity of St AndrewsSt AndrewsScotland

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