基于粒子群优化的模糊C一均值聚类算法研究

计算机科学 ›› 2012, Vol. 39 ›› Issue (9): 166-169.

基于粒子群优化的模糊C一均值聚类算法研究

王纵虎,刘志镜,陈东辉

(西安电子科技大学计算机学院西安710071)

出版日期:2018-11-16 发布日期:2018-11-16

Research of PSO-based Fuzzy C-means Clustering Algorithm

Online:2018-11-16 Published:2018-11-16

摘要/Abstract

摘要： 针对用模糊G均值聚类算法选择初始聚类中心敏感及模糊加权指数。对模糊G均值聚类算法的聚类性能影响较大等问题，利用粒子群优化算法的全局寻优能力强及收敛速度较快的特点，结合模糊G均值算法提出一种新的模糊聚类算法;采用了一种简单有效的粒子编码方法，将初始聚类中心和模糊加权指数二同时进行粒子群优化搜索，在得到最优适应度的同时，二也收敛到一个稳定的最优解，从而有效地解决了上述问题。算法在人工合成数据集和多个UCI数据集上都取得了较好的效果。

关键词: 聚类，模糊G均值聚类，粒子群优化，粒子编码，初始聚类中心

Abstract: Fuzzy C-mean algorithm is sensitive to initial centroid and the choice of fuzzy weighting exponent m plays an import role in clustering result PSO has the advantage of global optimization and good convergence speed. This paper proposed a new method by combining PSO and fuzzy GMeans to solve those problem. I3y a simple and effective particle encoding method, the best initial centroid and fuzzy weighting exponent were both searched in the process of PSO. Ex- periments on synthetic data sets and several UCI data sets achieve good results.

Key words: Clustering, FCM, PSO, Particle coding, Initial centroid

王纵虎,刘志镜,陈东辉. 基于粒子群优化的模糊C一均值聚类算法研究[J]. 计算机科学, 2012, 39(9): 166-169. https://doi.org/

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