A Valid Clustering Algorithm for High-dimensional Large Data Sets Based on Distributed Method

被引：0
|
作者：
Guo Xian e [1 ]
Yan Junmei [1 ]
机构：
[1] Math & Comp Sci Inst, Datong, Shanxi, Peoples R China
来源：
PROCEEDINGS OF 2009 INTERNATIONAL WORKSHOP ON INFORMATION SECURITY AND APPLICATION | 2009年
关键词：
fuzzy clustering; distributed method; genetic algorithm; fuzzy dissimilar matrix; large data sets; high dimension;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Data sets are randomly divided into several subsets, then fuzzy clustering method for a A high-dimensional datas based on genetic algorithm is proposed to cluster the subsets, by importing a fuzzy dissimilar matrix to express the dissimilar degree between any two datas, and initializing the high-dimensional samples to two-dimensional plane. Then iteratively optimize the coordinate value of two-dimensional plane using genetic algorithm, which makes the Euclidean distance between the two-dimensional plane approximate to the fuzzy dissimilar degree between samples gradually. At last cluster the two-dimensional datas using FCM algorithm, so avoid dependence of clustering validity on the space distribution of high-dimensional samples. Experimental results show the method has high quality result, and improves the clustering speed greatly.
引用
收藏
页码：1 / 6
页数：6
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