Null Models in Cluster Validation

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


A brief overview is given of the problem of validation in classification studies. Attention is concentrated on the specification of appropriate null models for data, with respect to which one may assess some cluster structure that has been obtained as the output of a clustering algorithm. In addition to standard null models, a discussion is given of ‘data-influenced’ null models, in which the precise form of the null hypothesis is influenced by characteristics of the data set under investigation. To illustrate the importance of specifying relevant null models, the behaviour of U-statistics under these null models is used to assess individual clusters found when data were classified using some standard clustering criteria implemented in an agglomerative algorithm.


Null Model Poisson Model Multivariate Normal Distribution Hierarchical Classification Cluster Validation 
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-Verlag Berlin · Heidelberg 1996

Authors and Affiliations

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

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