Data streaming with affinity propagation

Xiangliang Zhang*, Cyril Furtlehner, Michèle Sebag

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

66 Scopus citations

Abstract

This paper proposed StrAP (Streaming AP), extending Affinity Propagation (AP) to data steaming. AP, a new clustering algorithm, extracts the data items, or exemplars, that best represent the dataset using a message passing method. Several steps are made to build StrAP. The first one (Weighted AP) extends AP to weighted items with no loss of generality. The second one (Hierarchical WAP) is concerned with reducing the quadratic AP complexity, by applying AP on data subsets and further applying Weighted AP on the exemplars extracted from all subsets. Finally StrAP extends Hierarchical WAP to deal with changes in the data distribution. Experiments on artificial datasets, on the Intrusion Detection benchmark (KDD99) and on a real-world problem, clustering the stream of jobs submitted to the EGEE grid system, provide a comparative validation of the approach.

Original languageEnglish (US)
Title of host publicationMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2008, Proceedings
Pages628-643
Number of pages16
EditionPART 2
DOIs
StatePublished - Nov 19 2008
EventEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008 - Antwerp, Belgium
Duration: Sep 15 2008Sep 19 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5212 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherEuropean Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2008
CountryBelgium
CityAntwerp
Period09/15/0809/19/08

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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