基于粒子群优化的模糊C一均值聚类算法研究

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。

Research of PSO-based Fuzzy C-means Clustering Algorithm

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.