DocumentCode
2288666
Title
An initial independent and highly noise-resistant fuzzy possibilistic clustering algorithm
Author
Zhuang, Xinhua ; Zhao, Yunxin ; Huang, Yan ; Huang, Tong
Author_Institution
Dept. of Electr. & Comput. Eng., Missouri Univ., Columbia, MO, USA
fYear
1994
fDate
13-16 Apr 1994
Firstpage
237
Abstract
The possibilistic C-means (PCM) clustering algorithm is shown to be superior to the conventional fuzzy C-means (FCM) clustering algorithms. We attack several unsolved issues in applying the possibilistic approach to fuzzy clustering. An initial independent and highly noise resistant possibilistic clustering algorithm, named the novel possibilistic C-means (NPCM) clustering algorithm, is developed
Keywords
fuzzy set theory; noise; pattern recognition; probability; fuzzy C-means clustering; fuzzy possibilistic clustering algorithm; image processing; noise-resistant algorithm; novel possibilistic C-means clustering; pattern recognition; possibilistic C-means clustering; Algorithm design and analysis; Clustering algorithms; Image analysis; Image processing; Least squares methods; Pattern analysis; Pattern recognition; Phase change materials; Uncertainty; Working environment noise;
fLanguage
English
Publisher
ieee
Conference_Titel
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN
0-7803-1865-X
Type
conf
DOI
10.1109/SIPNN.1994.344923
Filename
344923
Link To Document