基于粒子群优化的模糊C-均值聚类算法研究

Research of PSO-based Fuzzy C-means Clustering Algorithm

针对用模糊C-均值聚类算法选择初始聚类中心敏感及模糊加权指数m对模糊C-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C-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m同时进行粒子群优化搜索,在得到最优适应度的同时,m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。

Fuzzy C-mean algorithm is sensitive to initial centroid and the choice of fuzzy weighting exponent mplays 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 C-Means to solve those problem.By a simple and effective particle encoding method,the best initial centroid and fuzzy weighting exponent were both searched in the process of PSO.Experiments on synthetic data sets and several UCI data sets achieve good results.