Research on Bisecting K-Means Clustering Algorithm Optimization and Parallelism
Junwei Zhang, Wang Nianbin, Huang Shaobin, Man Shi-ming · Jisuanji gongcheng · 2011
Considering the insufficiency of clustering speed which exists in the selecting the initial centroid of Bisecting K-Means(BKM) clustering algorithm,the idea of selecting the two patterns with distance maximum as the initial cluster centroid is implemented.An in-depth study and analysis is carried out on how to accelerate clustering in clustering system.According to the characteristics of BKM,the parallelism algorithm based on data parallelism and symmetric data-partition is put forward.Experimental results show that the improvement of algorithm gets ideal speedup performance and efficiency.