Genetic algorithm-based high-dimensional data clustering technique

Authors:
Hao-jun Sun;Lang-huan Xiong
Affiliations:
Department of Computer Science and Technology, Shantou University, Shantou, China;Department of Computer Science and Technology, Shantou University, Shantou, China
Venue:
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 1
Year:
2009

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Abstract

A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, is proposed in this paper. This approach searches feature subspace by genetic algorithms to find the effective clustering feature subspaces. The candidate features and cluster centers are binary encoded, and the degree of feature subspace contributes to subspace clustering is proposed as the fitness function. The experimental results indicate the feasibility and efficiency of the GA-HDclustering algorithm.