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<p><b>Abstract</b>—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.</p>
Clustering, hierarchical methods, context-based clustering, cluster isolation criteria, dissimilarity increments, model-based clustering.

A. L. Fred and J. M. Leit?, "A New Cluster Isolation Criterion Based on Dissimilarity Increments," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 25, no. , pp. 944-958, 2003.
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