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
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);
Conference_Titel :
System Science and Engineering (ICSSE), 2013 International Conference on
Conference_Location :
Budapest
Print_ISBN :
978-1-4799-0007-7
DOI :
10.1109/ICSSE.2013.6614679