標題: Comparing Hard and Fuzzy C-Means for Evidence-Accumulation Clustering
作者: Wang, Tsaipei
資訊工程學系
Department of Computer Science
公開日期: 2009
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.
URI: http://hdl.handle.net/11536/17680
http://dx.doi.org/10.1109/FUZZY.2009.5277122
ISBN: 978-1-4244-3596-8
DOI: 10.1109/FUZZY.2009.5277122
期刊: 2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3
起始頁: 468
結束頁: 473
顯示於類別:會議論文


文件中的檔案:

  1. 000274242600082.pdf