Combining multiple clustering systems

Authors:
Constantinos Boulis;Mari Ostendorf
Affiliations:
University of Washington, Seattle, WA;University of Washington, Seattle, WA
Venue:
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
Year:
2004

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Abstract

Three methods for combining multiple clustering systems are presented and evaluated, focusing on the problem of finding the correspondence between clusters of different systems. In this work, the clusters of individual systems are represented in a common space and their correspondence estimated by either clustering clusters or with Singular Value Decomposition. The approaches are evaluated for the task of topic discovery on three major corpora and eight different clustering algorithms and it is shown experimentally that combination schemes almost always offer gains compared to single systems, but gains from using a combination scheme depend on the underlying clustering systems.