基于进化策略的K-means聚类算法

针对K-means聚类算法易陷入局部极小以及K值选取的问题，提出一类基于进化策略的聚类算法，可以有效地搜索最优聚类中心和聚类个数K；还提出了确定K值范围的经验公式，以减小搜索空间，提高搜索效率，并给出了理论分析．相对遗传算法而言，本方法鳊码简单，种群较小．对Fishers iris数据集的仿真实验表明，该方法得到最优解的可能性比经典算法大得多．

K-means Clustering Based on Evolution Strategy

K-means clustering has two disadvantages. The first one is easy to be trapped in local minimum, and the another one is difficult to determine the number of clusters K. To address the problems, the paper proposes 3 new K-means algorithms based on Evolution Strategy. They own the easier coding scheme and the smaller population than Genetic Algorithm. These algorithms are applied to cluster Fisher's iris data set and work very well, especially when the priori Knowledge are insufficient.