Clustering method based on data division and partition  

作　　者：卢志茂 刘晨 S.Massinanke 张春祥 王蕾 

机构地区：[1]School of Information and Communication Engineering,Harbin Engineering University [2]School of Software,Harbin University of Science and Technology [3]School of Computer Science and Technology,Harbin Engineering University

出　　处：《Journal of Central South University》2014年第1期213-222,共10页中南大学学报（英文版）

基　　金：Projects(60903082,60975042)supported by the National Natural Science Foundation of China;Project(20070217043)supported by the Research Fund for the Doctoral Program of Higher Education of China

摘　　要：Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets(VLDS).In this work,a novel division and partition clustering method(DP) was proposed to solve the problem.DP cut the source data set into data blocks,and extracted the eigenvector for each data block to form the local feature set.The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector.Ultimately according to the global eigenvector,the data set was assigned by criterion of minimum distance.The experimental results show that it is more robust than the conventional clusterings.Characteristics of not sensitive to data dimensions,distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.Many classical clustering algorithms do good jobs on their prerequisite but do not scale well when being applied to deal with very large data sets （VLDS）. In this work, a novel division and partition clustering method （DP） was proposed to solve the problem. DP cut the source data set into data blocks, and extracted the eigenvector for each data block to form the local feature set. The local feature set was used in the second round of the characteristics polymerization process for the source data to find the global eigenvector. Ultimately according to the global eigenvector, the data set was assigned by criterion of minimum distance. The experimental results show that it is more robust than the conventional clusterings. Characteristics of not sensitive to data dimensions, distribution and number of nature clustering make it have a wide range of applications in clustering VLDS.

关 键 词：CLUSTERING DIVISION PARTITION very large data sets （VLDS） 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]