Super Large Data Sets Clustering by Means Radial Compression  

作　　者：LU Zhimao LIU Chen ZHANG Qi Massinanke Sambourou FAN Dongmei 

机构地区：[1]School of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China [2]Bamako University, Bamako, Republic of Mali

出　　处：《Chinese Journal of Electronics》2013年第2期335-340,共6页电子学报（英文版）

摘　　要：Clustering analysis is an effective technique for exploring data analysis which has been widely applied to varied tasks. Many classical clustering algorithms do good jobs on their prerequisite, but few of them are scal- able when applied to Very large data sets （VLDS）. In this study, a novel means radial compression clustering method is proposed to deal with the VLDS. First, the concept of means radial compression is defined to describe theoretical model. Next, mean merging is defined and it is proved that the process of mean merging is an efficient method for the implementation of means radial compression. Then, the members will be assigned to the suitable clusters based on the minimum distance between each member and the centers that is found by means radial compression clus- tering. The experimental results show that means ra- dial compression algorithm can make better solutions com- pared with the most well known clustering algorithms as K-means clustering, affinity propagation clustering~ hier- archical clustering with time complexity of O（n）.

关 键 词：Clustering analysis Means radial com- pression Very large data sets （VLDS）. 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] TS103.829[自动化与计算机技术—计算机科学与技术]