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

Tsaipei Wang*

*此作品的通信作者

研究成果: Conference contribution同行評審

5 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題2009 IEEE International Conference on Fuzzy Systems - Proceedings
頁面468-473
頁數6
DOIs
出版狀態Published - 2009
事件2009 IEEE International Conference on Fuzzy Systems - Jeju Island, Korea, Republic of
持續時間: 20 8月 200924 8月 2009

出版系列

名字IEEE International Conference on Fuzzy Systems
ISSN(列印)1098-7584

Conference

Conference2009 IEEE International Conference on Fuzzy Systems
國家/地區Korea, Republic of
城市Jeju Island
期間20/08/0924/08/09

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