一种改进的K-means算法

An Improved K-means Algorithm

聚类分析在科研和商业应用中都有着非常重要的应用,K means算法是聚类方法中常用的一种划分方法。随着数据量的增加,K means算法的局限性日益突出。基于取样的划分思想,提出了一种改进的K means算法,在一定程度上避免了聚类结果陷入局部解的现象,减少了原始K means算法因采用误差平方和准则函数而出现将大的聚类簇分割开的情况,仿真实验结果表明:改进后的K means算法优于原始算法,并且稳定性更好。

Clustering analysis plays an important role in scientific research and commercial application. Kmeans algorithm is a widely used partition method in clustering. As the datasets scale increases rapidly, it is difficult to use Kmeans to deal with massive data. An improved Kmeans algorithm is presented, which can avoid getting into locally optimal solution in some degree, and reduce the probability of dividing a big cluster into two or more ones owing to the adoption of Jc. The experiments demonstrate the improved Kmeans is more stable and more accurate.