Clustering pair-wise dissimilarity data into partially ordered sets

Authors:
Jinze Liu;Qi Zhang;Wei Wang;Leonard McMillan;Jan Prins
Affiliations:
University of North Carolina, Chapel Hill, NC;University of North Carolina, Chapel Hill, NC;University of North Carolina, Chapel Hill, NC;University of North Carolina, Chapel Hill, NC;University of North Carolina, Chapel Hill, NC
Venue:
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2006

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Abstract

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.