基于Kd树改进的高效K-means聚类算法

针对经典的K-means算法在多维数据聚类效率上还有待提高的问题，本文提出一种称为CK-means的改进聚类算法。该算法在k-means算法的基础上，通过引入Kd树空间数据结构，初始聚类中心从多维数据某一维的区间等间隔集中选取，以及在数据对象分配过程中采用剪枝策略来提高算法的运行效率。实验结果表明，CK-means聚类算法较经典的k-means聚类算法运行效率更高。

An Improved K-means Clustering Algorithm With High Efficiency Based on the Kd Tree

This paper proposed a modified clustering algorithm called ck-means for the question that the classic k-means algorithm is expected to be improved in the clustering efficiency of multidimensional data. Based on the k-means algorithm, the pk-means algorithm introduces a spatial data structure called kd tree, selects the initial clustering center points from the equal-interval partition point sets of a certain dimension of the multidimensional data, and adopts pruning strategy during the points assigning process, to improve the clustering efficiency of the algorithm. Experimental results show that the clustering efficiency of ck-means is higher than that of the k-means .