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

Authors
Journal
Pattern Recognition
0031-3203
Publisher
Elsevier
Publication Date
Volume
26
Issue
9
Identifiers
DOI: 10.1016/0031-3203(93)90145-m
Keywords
  • Agglomerative Clustering
  • Metricity
  • Combinatoriality
  • Monotonicity
  • Stopping Criteria
  • Goodness
  • Upgma
  • Furthest Neighbor
  • Sequence Similarity
  • Representatives
Disciplines
  • Mathematics

Abstract

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.

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