Comparing hard and fuzzy C-means for evidence-accumulation clustering

Tsaipei Wang*

*Corresponding author for this work

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

5 Scopus citations

Abstract

There exist a multitude of fuzzy clustering algorithms with well understood properties and benefits in various applications. However, there has been very little analysis on using fuzzy clustering algorithms to generate the base clusterings in cluster ensembles. This paper focuses on the comparison of using hard and fuzzy c-means algorithms in the well known evidence-accumulation framework of cluster ensembles. Our new findings include the observations that the fuzzy c-means requires much fewer base clusterings for the cluster ensemble to converge, and is more tolerant of outliers in the data. Some insights are provided regarding the observed phenomena in our experiments.

Original languageEnglish
Title of host publication2009 IEEE International Conference on Fuzzy Systems - Proceedings
Pages468-473
Number of pages6
DOIs
StatePublished - 10 Dec 2009
Event2009 IEEE International Conference on Fuzzy Systems - Jeju Island, Korea, Republic of
Duration: 20 Aug 200924 Aug 2009

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2009 IEEE International Conference on Fuzzy Systems
CountryKorea, Republic of
CityJeju Island
Period20/08/0924/08/09

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  • Cite this

    Wang, T. (2009). Comparing hard and fuzzy C-means for evidence-accumulation clustering. In 2009 IEEE International Conference on Fuzzy Systems - Proceedings (pp. 468-473). [5277122] (IEEE International Conference on Fuzzy Systems). https://doi.org/10.1109/FUZZY.2009.5277122