吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (1): 246-251.doi: 10.13229/j.cnki.jdxbgxb201601037

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Kernel-based fuzzy C-means clustering method based on parameter optimization

LIU Yun, LIU Fu, HOU Tao, ZHANG Xiao   

  1. College of Communications Engineering, Jilin University, Changchun 130022, China
  • Received:2014-09-26 Online:2016-01-30 Published:2016-01-30

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Abstract: Kernel-based Fuzzy C-means Clustering Method (KFCM) is a common method for data clustering. The performance of KFCM is greatly affected by the parameter of the kernel function, while the selection of kernel parameter is extremely difficult in practice. To solve this problem, a Parameter Optimization-based KFCM (POKFCM) is proposed according to the idea that the distances between samples of the same class are closer than the distance between samples from different classes. First, initial clustering of dataset is completed by K-means method. Then the optimal kernel parameter is determined by calculating the distance similarity between the actual kernel matrix and ideal kernel matrix. Finally, the optimal kernel parameter is applied to KFCM. Clustering experiment results of six UCI datasets illustrate that POKFCM can effectively improve the clustering performance of KFCM.

Key words: artificial intelligence, kernel-based fuzzy C-means, kernel function, parameter optimization

CLC Number: 

  • TP391
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