A versatile clustering algorithm with objective function and objective measure

  • Prewitt J
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Abstract

A computer program for nonparametric cluster synthesis, using similarity rather than maximum likelihood as the basis for class membership, is presented. The algorithm utilizes recursive computations to develop a hierarchy or tree of nested clusters. The major components of the program are: (1) a (dis)similarity function. (2) a grouping or merger strategy, based on optimizing a dynamic objective function, and (3) a halting criterion, based on evaluating a dynamic objective measure. Program options permit variations of data normalization, measures of similarity, and clustering strategy. A variety of hard-copy summaries and displays are available to the user. An illustrative application to the classification of human mitotic chromosomes is included. © 1972.

Author-supplied keywords

  • (Dis)similarity measures
  • Classification
  • Cluster analysis
  • Cluster synthesis
  • Clustering
  • Merger strategies
  • Nonparametric classification
  • Taxonomy

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Authors

  • Judith M.S. Prewitt

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