Title :
Comparison of Four Kinds of Fuzzy C-Means Clustering Methods
Author_Institution :
Qingdao Univ. of Sci. & Technol., Qingdao, China
Abstract :
Advantages of None Euclidean Relational Fuzzy C-means (NERFCM) is analysed, by which four Fuzzy C-means (FCM) clustering algorithms are compared, which includes Fuzzy C-means (FCM) and traditional Relational Fuzzy C-means (RFCM) and None Euclidean Relational Fuzzy C-means (NERFCM) and Any Relational Fuzzy C-means (ARFCM). Their common points and different limitations on usage are discussed, finally an optimal clustering algorithm is chosen and its application on human posture classification is implemented, and experiments prove its efficiency and sensitivity.
Keywords :
fuzzy set theory; pattern classification; pattern clustering; FCM clustering algorithms; NERFCM; any relational fuzzy c-means; fuzzy c-means clustering algorithms; fuzzy c-means clustering methods; human posture classification; none Euclidean relational fuzzy c-means; optimal clustering algorithm; Algorithm design and analysis; Classification algorithms; Clustering algorithms; Convergence; Humans; Prototypes; Radio frequency; NERF C-means; posture classification; relational fuzzy C-means;
Conference_Titel :
Information Processing (ISIP), 2010 Third International Symposium on
Conference_Location :
Qingdao
Print_ISBN :
978-1-4244-8627-4
DOI :
10.1109/ISIP.2010.133
