DocumentCode
1660972
Title
Comparison of Four Kinds of Fuzzy C-Means Clustering Methods
Author
Wang, Zengfeng
Author_Institution
Qingdao Univ. of Sci. & Technol., Qingdao, China
fYear
2010
Firstpage
563
Lastpage
566
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;
fLanguage
English
Publisher
ieee
Conference_Titel
Information Processing (ISIP), 2010 Third International Symposium on
Conference_Location
Qingdao
Print_ISBN
978-1-4244-8627-4
Type
conf
DOI
10.1109/ISIP.2010.133
Filename
5669085
Link To Document