Distributed and incremental clustering based on weighted affinity propagation

Xiangliang Zhang, Cyril Furtlehner, Michèle Sebag

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


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. © 2008 The authors and IOS Press. All rights reserved.
Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
PublisherIOS PressNieuwe Hemweg 6BAmsterdam1013 BG
Number of pages12
ISBN (Print)9781586038939
StatePublished - Jan 1 2008
Externally publishedYes

ASJC Scopus subject areas

  • Artificial Intelligence


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