Distributed and Incremental Clustering Based on Weighted Affinity Propagation - Archive ouverte HAL Access content directly
Conference Papers Year : 2008

Distributed and Incremental Clustering Based on Weighted Affinity Propagation

(1) , (1) , (1)
1
Cyril Furtlehner
  • Function : Author
  • PersonId : 849672
Michèle Sebag
  • Function : Author
  • PersonId : 836537

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.
Fichier principal
Vignette du fichier
STAIRS08_vfinal.pdf (121.2 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

inria-00287378 , version 1 (11-06-2008)

Identifiers

  • HAL Id : inria-00287378 , version 1

Cite

Xiangliang Zhang, Cyril Furtlehner, Michèle Sebag. Distributed and Incremental Clustering Based on Weighted Affinity Propagation. the fourth European Starting AI Researcher Symposium (STAIRS), Jul 2008, Patras, Greece. ⟨inria-00287378⟩
118 View
342 Download

Share

Gmail Facebook Twitter LinkedIn More