Title :
Comparing hard and fuzzy c-means for evidence-accumulation clustering
Author_Institution :
Dept. of Comput. Sci., Nat. Chiao Tung Univ., Hsinchu, Taiwan
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.
Keywords :
convergence; fuzzy set theory; pattern clustering; unsupervised learning; base clustering; cluster ensemble; convergence; evidence-accumulation framework; hard-fuzzy c-means clustering algorithm; outlier tolerance; unsupervised learning; Algorithm design and analysis; Bipartite graph; Clustering algorithms; Clustering methods; Computer science; Couplings; Data mining; Partitioning algorithms; Prototypes; Robustness;
Conference_Titel :
Fuzzy Systems, 2009. FUZZ-IEEE 2009. IEEE International Conference on
Conference_Location :
Jeju Island
Print_ISBN :
978-1-4244-3596-8
Electronic_ISBN :
1098-7584
DOI :
10.1109/FUZZY.2009.5277122
