Clustering under a hypothesis of smooth dissimilarity increments

The problem of cluster defining criteria has been addressed in various forms. In the paper, a cluster isolation criterion is proposed, underlying an hypothesis of smooth dissimilarity increments between neighboring patterns within a cluster. This isolation criterion is merged in a hierarchical agglomerative clustering algorithm, producing a data partitioning and simultaneous accessibility to the intrinsic data inter-relationships in terms of a dendrogram-type graph. By defining adequate dissimilarity measures, the algorithm is applied to vector based pattern analysis and to categorization of structural patterns. Both simulated data and real applications, in the context of automatic analysis of contour images, are presented to illustrate and evaluate the method. Examples demonstrate the versatility of the method in identifying arbitrary shape and size clusters, intrinsically finding the number of clusters