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
收藏
页码：769 / 772
页数：4
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