DocumentCode
3263399
Title
Possibilistic c-regression models clustering algorithm
Author
Chung-Chun Kung ; Hong-Chi Ku ; Jui-Yiao Su
Author_Institution
Dept. of Electr. Eng., Tatung Univ., Taipei, Taiwan
fYear
2013
fDate
4-6 July 2013
Firstpage
297
Lastpage
302
Abstract
The purpose of this paper is to apply the possibilistic c-means (PCM) clustering algorithm to the fuzzy c-regression models (FCRM) clustering algorithm and propose a new clustering algorithm named possibilistic c-regression models (PCRM). The PCRM clustering algorithms relaxes the column sum constrain result in each cluster, it will alleviate the noisy data effectively. Finally, the simulation examples are provided to demonstrate the effectiveness of the PCRM clustering algorithm.
Keywords
fuzzy set theory; pattern clustering; regression analysis; FCRM clustering algorithm; PCM clustering algorithm; PCRM clustering algorithms; column sum constrain; fuzzy c-regression model clustering algorithm; noisy data; possibilistic c-regression models clustering algorithm; Clustering algorithms; Conferences; Equations; Noise; Noise measurement; Partitioning algorithms; Signal processing algorithms; fuzzy c-regression models (FCRM); fuzzy clustering; possibilistic c-means (PCM);
fLanguage
English
Publisher
ieee
Conference_Titel
System Science and Engineering (ICSSE), 2013 International Conference on
Conference_Location
Budapest
ISSN
2325-0909
Print_ISBN
978-1-4799-0007-7
Type
conf
DOI
10.1109/ICSSE.2013.6614679
Filename
6614679
Link To Document