PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Distributed and Incremental Clustering Based on Weighted Affinity Propagation
Xiangliang Zhang, Cyril Furtlehner and Michele Sebag
In: Proceedings of the Fourth Starting AI Researchers(STAIRS), 21-25 July 2008, Patras, Greece.

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.

EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:4747
Deposited By:Xiangliang Zhang
Deposited On:24 March 2009