Feature Selection for Clustering using Genetic Algorithms
Amal BOURAOUI, Sahar REGAIEG, Salma JAMOUSSI, Yassine BEN AYED
Journal of Intelligent Computing, Vol. 4, No. 2, 2013.
DOI: 
Link: http://www.dline.info/jic/fulltext/v4n2/2.pdf
Abstract:
The present article introduces a genetic algorithm based method to select interesting features in a clustering framework. Indeed, we used the evolutionary paradigm to explore many subsets of attributes and evaluate them according to inertia criteria when the K-Means clustering method is used in different way to data clusters. The proposed method is applied to many benchmark datasets. The experimental obtained results show the efficiency of the proposed method where features were reduced by more than 50% and the efficiency of the clustering has been improved.