A GA-based Feature Selection for High-dimensional Data Clustering

被引：5

作者：

Sun, Mei ^{[1
]}

Xiong, Langhuan ^{[1
]}

Sun, Haojun ^{[1
]}

Jiang, Dazhi ^{[1
]}

机构：

[1] Shantou Univ, Dept Comp Sci & Technol, Shantou 515063, Peoples R China

来源：

THIRD INTERNATIONAL CONFERENCE ON GENETIC AND EVOLUTIONARY COMPUTING | 2009年

关键词：

feature selection; clustering; genetic algorithms; high-dimensional data;

D O I：

10.1109/WGEC.2009.140

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

High-dimensional data clustering is an open problem in modern data mining. This paper proposed a new genetic algorithm-based feature selection for high-dimensional data clustering, called GA-FSFclustering. This approach searches effective feature subsets for clustering in all features by genetic algorithm. The candidate features and cluster centers are real number encoded. A new criterion for evaluating feature subsets is employed as the fitness function. The experimental results indicate the feasibility and efficiency of the GA-FSFclustering algorithm.

引用

页码：769 / 772

页数：4

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