ISBN: 978-981-11-3671-9 DOI: 10.18178/wcse.2017.06.019
Kernel FCM Based on Simulated Annealing Algorithm
Abstract— As everyone knows,birds of a feather flock together,which reveals the essence of
clustering.Fuzzy c-means clustering algorithm(FCM) in many algorithms of clustering play a significant role
in unsupervised learning.However,FCM still has the following three defects—unknown number of
clusters,unstable process of clustering and discoveries of outliers . Aimed at these defects existed in the
FCM,a kernel based fuzzy c-means clustering algorithm is proposed to optimize conventional FCM,based on
simulated annealing algorithm which takes advantage of simulated annealing algorithm(SA) and kernel
technology.The proposed algorithm can improve these shortcomings, but it is not ideal. Our explanation of
the results is that the dataset IRIS is relatively simple and the proposed algorithm is an attempt to the original
algorithm when realizing global optimization. Our suggestion is that if the dataset contains amount of
duplicate data, please apply the proposed algorithm. Anyway, the proposed algorithm is an attempt when
traditional ones can’t find the global optimization, so it will spend more time.
Index Terms— clustering, FCM;kernel methods, simulated annealing, SAKFCM
Chengwei Jiang, Xiaoyan Cui, Tongliang Li
Department of Automation, Beijing University of Posts and Telecommunications, CHINA
Kaijian Zhang
School of Mechanical and Electronic Control Engineering, Beijing Jiaotong University, CHINA
Cite: Chengwei Jiang, Xiaoyan Cui, Kaijian Zhang, Tongliang Li, "Kernel FCM Based on Simulated Annealing Algorithm," Proceedings of 2017 the 7th International Workshop on Computer Science and Engineering, pp. 110-115, Beijing, 25-27 June, 2017.