Distributed and Incremental Clustering Based on Weighted Affinity Propagation

Authors:
Xiangliang Zhang;Cyril Furtlehner;Michèle Sebag
Affiliations:
Laboratoire de Recherche en Informatique, CNRS UMR 8623 & INRIA Saclay, Bââtiment 490, University Paris Sud 11, 91405-Orsay Cedex, France;Laboratoire de Recherche en Informatique, CNRS UMR 8623 & INRIA Saclay, Bââtiment 490, University Paris Sud 11, 91405-Orsay Cedex, France;Laboratoire de Recherche en Informatique, CNRS UMR 8623 & INRIA Saclay, Bââtiment 490, University Paris Sud 11, 91405-Orsay Cedex, France
Venue:
Proceedings of the 2008 conference on STAIRS 2008: Proceedings of the Fourth Starting AI Researchers' Symposium
Year:
2008

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Abstract

A new clustering algorithm Affinity Propagation (AP) is hindered by its quadratic complexity. The Weighted Affinity Propagation (WAP) proposed in this paper is used to eliminate this limitation, support two scalable algorithms. Distributed AP clustering handles large datasets by merging the exemplars learned from subsets. Incremental AP extends AP to online clustering of data streams. The paper validates all proposed algorithms on benchmark and on real-world datasets. Experimental results show that the proposed approaches offer a good trade-off between computational effort and performance.