document: Clustering pair-wise dissimilarity data into partially ordered sets Ontologies represent data relationships as hierarchies of possibly overlapping classes . Ontologies are closely related to clustering hierarchies , and in this article we explore this relationship in depth . In particular , we examine the space of ontologies that can be generated by pairwise dissimilarity matrices . We demonstrate that classical clustering algorithms , which take dissimilarity matrices as inputs , do not incorporate all available information . In fact , only special types of dissimilarity matrices can be exactly preserved by previous clustering methods . We model ontologies as a partially ordered set ( poset ) over the subset relation . In this paper , we propose a new clustering algorithm , that generates a partially ordered set of clusters from a dissimilarity matrix .
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