Clustering similarity comparison using density profiles

Authors:
Eric Bae;James Bailey;Guozhu Dong
Affiliations:
NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, Australia;NICTA Victoria Laboratory, Department of Computer Science and Software Engineering, University of Melbourne, Australia;Department of Computer Science and Engineering, Wright State University
Venue:
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Year:
2006

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Abstract

The unsupervised nature of cluster analysis means that objects can be clustered in many ways, allowing different clustering algorithms to generate vastly different results. To address this, clustering comparison methods have traditionally been used to quantify the degree of similarity between alternative clusterings. However, existing techniques utilize only the point memberships to calculate the similarity, which can lead to unintuitive results. They also cannot be applied to analyze clusterings which only partially share points, which can be the case in stream clustering. In this paper we introduce a new measure named ADCO, which takes into account density profiles for each attribute and aims to address these problems. We provide experiments to demonstrate this new measure can often provide a more reasonable similarity comparison between different clusterings than existing methods.