DocumentCode :
1561022
Title :
A noise-resistant fuzzy clustering approach with probabilistic typicalities
Author :
Guan, Tao ; Feng, BoQin
Author_Institution :
Sch. of Electron. & Inf. Eng., Xi´´an Jiaotong Univ., China
Volume :
3
fYear :
2004
Firstpage :
2327
Abstract :
Probabilistic typicalities are different concepts comparing with probabilistic memberships in fuzzy clustering. They give another description of relation between data points and clustering centers. In terms of function optimization techniques, this paper presents a noise-resistant fuzzy clustering approach with probabilistic typicalities. Moreover, the usage of exponential functions greatly enlarges the relativity of points to centers based on the Euclidean distance. At last, its variety is also presented and behaves better on the IRIS data than the existed methods in computation precision. Experimental comparisons on noisy data and IRIS data show that our approach is hardly affected by noise and more accurate in computing the cluster centers than existed methods.
Keywords :
fuzzy set theory; noise; optimisation; pattern clustering; probabilistic logic; probability; Euclidean distance; IRIS data; clustering centers; data points; exponential functions; function optimization techniques; noise resistant fuzzy clustering method; noisy data; probabilistic memberships; probabilistic typicalities; Clustering algorithms; Clustering methods; Euclidean distance; Image processing; Iris; Noise robustness; Partitioning algorithms; Pattern recognition; Phase change materials; Uniform resource locators;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Control and Automation, 2004. WCICA 2004. Fifth World Congress on
Print_ISBN :
0-7803-8273-0
Type :
conf
DOI :
10.1109/WCICA.2004.1342006
Filename :
1342006
Link To Document :
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