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A new cluster isolation criterion based on dissimilarity increments

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2 Author(s)
A. L. N. Fred ; Instituto de Telecomunicaoes, Instituto Superior Tecnico, Lisbon, Portugal ; J. M. N. Leitao

This paper addresses the problem of cluster defining criteria by proposing a model-based characterization of interpattern relationships. Taking a dissimilarity matrix between patterns as the basic measure for extracting group structure, dissimilarity increments between neighboring patterns within a cluster are analyzed. Empirical evidence suggests modeling the statistical distribution of these increments by an exponential density; we propose to use this statistical model, which characterizes context, to derive a new cluster isolation criterion. The integration of this criterion in a hierarchical agglomerative clustering framework produces a partitioning of the data, while exhibiting data interrelationships in terms of a dendrogram-type graph. The analysis of the criterion is undertaken through a set of examples, showing the versatility of the method in identifying clusters with arbitrary shape and size; the number of clusters is intrinsically found without requiring ad hoc specification of design parameters nor engaging in a computationally demanding optimization procedure.

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IEEE Transactions on Pattern Analysis and Machine Intelligence  (Volume:25 ,  Issue: 8 )