By Topic

A GA-Based Feature Selection for High-Dimensional Data Clustering

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Mei Sun ; Dept. of Comput. Sci. & Technol., Shantou Univ., Shantou, China ; Langhuan Xiong ; Haojun Sun ; Dazhi Jiang

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.

Published in:

Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on

Date of Conference:

14-17 Oct. 2009