DocumentCode
3187070
Title
Improvement and optimization of a fuzzy C-means clustering algorithm
Author
Shen, Yi ; Shi, Hong ; Zhang, Jian Qiu
Author_Institution
Dept. of Control Sci. & Eng., Harbin Inst. of Technol., China
Volume
3
fYear
2001
fDate
2001
Firstpage
1430
Abstract
In this paper, an improved FCM clustering algorithm is proposed. Unlike a traditional FCM clustering algorithm whose convergence is sensitive to its initial parameters, the proposed algorithm based on fuzzy decision theory can automatically and adaptively select these parameters with optimal values. The simulation results indicate that the modified algorithm not only overcomes the ill phenomena of the FCM algorithms available now, but also is robust to the selection of the weighting constants
Keywords
decision theory; fuzzy systems; nonlinear systems; optimisation; pattern clustering; statistical analysis; convergence; fuzzy C-means clustering algorithm; fuzzy decision theory; optimisation; simulation; weighting constant; weighting constants; Clustering algorithms; Convergence; Decision theory; Fuzzy sets; Image processing; Image recognition; Learning systems; Pattern analysis; Pattern recognition; Robustness;
fLanguage
English
Publisher
ieee
Conference_Titel
Instrumentation and Measurement Technology Conference, 2001. IMTC 2001. Proceedings of the 18th IEEE
Conference_Location
Budapest
ISSN
1091-5281
Print_ISBN
0-7803-6646-8
Type
conf
DOI
10.1109/IMTC.2001.929440
Filename
929440
Link To Document