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Modified affinity propagation clustering

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

4 Scopus citations

Abstract

Affinity propagation clustering is an efficient clustering technique that does not require prior knowledge of the number of clusters. However, it sets the input preferences without considering data set distribution and competition in the former iteration is ignored when updating messages passing between data points. This paper presents a modified affinity propagation algorithm. Firstly, preference for each data point to serve as an exemplar is computed self-adaptively based on data set distribution; then encouragement and chastisement mechanism is introduced for updating message of availability. Experimental results on standard data sets and synthetic data sets demonstrate feasibility and effectiveness of the proposed algorithm.

Original languageEnglish
Title of host publication2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages505-509
Number of pages5
ISBN (Electronic)9781479954032
DOIs
StatePublished - 3 Sep 2014
Externally publishedYes
Event2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Xi'an, China
Duration: 9 Jul 201413 Jul 2014

Publication series

Name2014 IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014 - Proceedings

Conference

Conference2nd IEEE China Summit and International Conference on Signal and Information Processing, IEEE ChinaSIP 2014
Country/TerritoryChina
CityXi'an
Period9/07/1413/07/14

Keywords

  • Affinity Propagation
  • Data set Distribution
  • Encouragement and Chastisement
  • Preference

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