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
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;
Conference_Titel :
Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on
Print_ISBN :
0-7803-1865-X
DOI :
10.1109/SIPNN.1994.344923
