Kernel-based fuzzy c-means clustering algorithm based on genetic algorithm

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• 作者：Yi Ding ^{teacher.dingyi@live.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae ; Xian Fu ; ^{teacher.fu@live.com" class="auth_mail" title="E-mail the corresponding author}Author Vitae
• 关键词：Fuzzy clustering ; Fuzzy c-means clustering ; Kernel-based fuzzy c-means ; Genetic algorithm
• 刊名：Neurocomputing
• 出版年：2016
• 出版时间：5 May 2016
• 年：2016
• 卷：188
• 期：Complete
• 页码：233-238
• 全文大小：697 K

文摘

Fuzzy c-means clustering algorithm (FCM) is a method that is frequently used in pattern recognition. It has the advantage of giving good modeling results in many cases, although, it is not capable of specifying the number of clusters by itself. Aimed at the problems existed in the FCM clustering algorithm, a kernel-based fuzzy c-means (KFCM) is clustering algorithm is proposed to optimize fuzzy c-means clustering, based on the Genetic Algorithm (GA) optimization which is combined of the improved genetic algorithm and the kernel technique (GAKFCM). In this algorithm, the improved adaptive genetic algorithm is used to optimize the initial clustering center firstly, and then the KFCM algorithm is availed to guide the categorization, so as to improve the clustering performance of the FCM algorithm. In the paper, Matlab is used to realize the simulation, and the performance of FCM algorithm, KFCM algorithm and GAKFCM algorithm is testified by test datasets. The results proved that the GAKFCM algorithm proposed overcomes FCM׳s defects efficiently and improves the clustering performance greatly.