Elsevier

Pattern Recognition

Volume 26, Issue 9, September 1993, Pages 1395-1406
Pattern Recognition

General formulation and evaluation of agglomerative clustering methods with metric and non-metric distances

https://doi.org/10.1016/0031-3203(93)90145-MGet rights and content

Abstract

Agglomerative clustering methods with stopping criteria are generalized. Clustering-related concepts are rigorously formulated with special consideration on metricity of object space. A new definition of combinatoriality is given, and a stronger proposition of monotonicity is proven. Specializations of the general method are applied to non-attributive non-metric and attributive pseudometric representations of biosequences. The furthest neighbor method is shown suitable for non-metric use. In metric object space, four inter-clusteral distance functions, including a new truly context sensitive method, are compared using a method-independent goodness criterion. For biosequence clustering, the new method overcomes the UPGMA, UPGMC, and furthest neighbor methods.

References (48)

  • T.F Smith et al.

    Identification of common molecular subsequences

    J. Molec. Biol.

    (1981)
  • P Argos et al.

    Sensitivity comparison of protein amino acid sequences

    Meth. Enzym.

    (1990)
  • M Vihinen

    Simultaneous comparison of several sequences

    Meth. Enzym.

    (1990)
  • M.H Klapper

    The independent distribution of amino acid near neighbor pairs into polypeptides

    Biochem. Biophys. Res. Commun.

    (1977)
  • T Kurita

    an efficient agglomerative clustering algorithm using a heap

    Pattern Recognition

    (1991)
  • D.J Hand

    Discrimination and Classification

    (1981)
  • J.A Hartigan

    Clustering Algorithms

    (1975)
  • H Späth

    Cluster Analysis Algorithms

    (1980)
  • H Späth

    Cluster Dissection and Analysis

    (1985)
  • C Zahn

    Graph-theoretical methods for detecting and describing gestalt clusters

    IEEE Trans. Comput.

    (1971)
  • K.M Cunningham et al.

    Evaluation of hierarchical grouping techniques, a preliminary study

    Comput. J.

    (1972)
  • G.N Lance et al.

    A general theory of classificatory sorting strategies. I. Hierarchical systems

    Comput. J.

    (1967)
  • P.H.A Sneath et al.

    Numerical Taxonomy

    (1973)
  • E.A Patrick

    Fundamentals of Pattern Recognition

    (1972)
  • Cited by (0)

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