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Distributed and Incremental Clustering Based on Weighted Affinity Propagation
Xiangliang Zhang, Cyril Furtlehner, Michèle Sebag
199 - 210
10.3233/978-1-58603-893-9-199
Frontiers in Artificial Intelligence and Applications
Volume 179: STAIRS 2008
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.

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