TY - GEN
T1 - Comparing hard and fuzzy C-means for evidence-accumulation clustering
AU - Wang, Tsaipei
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=71249128748&partnerID=8YFLogxK
U2 - 10.1109/FUZZY.2009.5277122
DO - 10.1109/FUZZY.2009.5277122
M3 - Conference contribution
AN - SCOPUS:71249128748
SN - 9781424435975
T3 - IEEE International Conference on Fuzzy Systems
SP - 468
EP - 473
BT - 2009 IEEE International Conference on Fuzzy Systems - Proceedings
T2 - 2009 IEEE International Conference on Fuzzy Systems
Y2 - 20 August 2009 through 24 August 2009
ER -