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. Categories and Subject Descriptors: H.2.8 [Database Management]: Database Applications - Data Mining General Terms: Algorithms

• Type: Conference
• Year: 2006
• Where: KDD
• Authors: Jinze Liu, Qi Zhang, Wei Wang 0010, Leonard McMillan, Jan Prins
• Keywords: Classical Clustering Algorithms, Data Mining, KDD 2006, Ontologies Represent Data, Pairwise Dissimilarity Matrices