An online clustering algorithm

Kan Li*, Fenglan Yao, Ruipeng Liu

*Corresponding author for this work

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

7 Citations (Scopus)

Abstract

This paper presents a new online clustering algorithm called SAFN which is used to learn continuously evolving clusters from non-stationary data. The SAFN uses a fast adaptive learning procedure to take into account variations over time. In non-stationary and multi-class environment, the SAFN learning procedure consists of five main stages: creation, adaptation, mergence, split and elimination. Experiments are carried out in three kinds of datasets to illustrate the performance of the SAFN algorithm for online clustering. Compared with SAKM algorithm, SAFN algorithm shows better performance in accuracy of clustering and multi-class high-dimension data.

Original languageEnglish
Title of host publicationProceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011
Pages1104-1108
Number of pages5
DOIs
Publication statusPublished - 2011
Event2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11 - Shanghai, China
Duration: 26 Jul 201128 Jul 2011

Publication series

NameProceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011
Volume2

Conference

Conference2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11
Country/TerritoryChina
CityShanghai
Period26/07/1128/07/11

Keywords

  • Online clustering
  • non-stationary data
  • self-adaptive feed-forward neural network
  • similarity measure

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