摘要
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. Kmeans algorithm is a widely used partition method in clustering. As the datasets scale increases rapidly, it is difficult to use Kmeans to deal with massive data. An improved Kmeans 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 Jc. The experiments demonstrate the improved Kmeans is more stable and more accurate.
出处
《计算机应用》
CSCD
北大核心
2003年第8期31-33,60,共4页
journal of Computer Applications