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  1. (1. 西安电子科技大学 电子工程学院, 陕西 西安 710071;
    2. 深圳大学 信息工程学院, 广东 深圳 518060)

  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2004-08-20 发布日期:2004-08-20

A study of a new fuzzy clustering algorithm based on the kernel method

WU Zhong-dong1;GAO Xin-bo1;XIE Wei-xin2


  1. (1. School of Electronic Engineering, Xidian Univ., Xi'an 710071, China;
    2. Collgeg of Information Engineering, Shenzhen Univ., Shenzhen 518060, China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2004-08-20 Published:2004-08-20

摘要: 将核方法的思想推广到模糊C-均值算法,构造了基于核函数的模糊核C-均值算法,使其能够聚类非超球体数据、被噪声污染数据、多种模式原型混合数据、不对称数据等多种数据结构,并指出一阶多项式模糊核C-均值算法等价于模糊C-均值算法.人工和实际数据的实验结果表明,与模糊C-均值算法相比,模糊核C-均值算法在多种数据结构条件下可以有效地进行聚类.

关键词: 聚类分析, 模糊C-均值, 核方法, 无监督学习

Abstract: We present a fuzzy kernel C-means clustering algorithm(FKCM) which is a generalization of the conventional fuzzy C-means clustering algorithm(FCM). This new FKCM algrotihm integrates FCM with the Mercer kernel function and can cluster non-hyperspherical data structure, data with noise, mixed data structure of multi pattern prototypes, asymmetric data structure, etc. This generalization can obviously improve the performance of the fuzzy C-means clustering algorithm. It is pointed out that the FKCM algorithm with the first-order polynomial kernel function is equivalent to the FCM algorithm. The results of experiments on the artificial and real data show that the fuzzy kernel C-means clustering algorithm can effectively cluster on data with diversiform structures in contrast to the fuzzy C-means clustering algorithm.

Key words: clustering analysis, fuzzy C-means algorithm, kernel-based method, unsupervised learning


  • TP391.41