Automatic Feature Subset Selection using Genetic Algorithm for Clustering

Feature subset selection is a process of selecting a subset of minimal, relevant features and is a pre processing technique for a wide variety of applications. High dimensional data clustering is a challenging task in data mining. Reduced set of features helps to make the patterns easier to understand. Reduced set of features are more significant if they are application specif ic. Almost all existing feature subset selection algorithms are not automatic and are not application specific. This paper made an attempt to find the feature subset for optimal clusters while clustering. The proposed Automatic Feature Subset Selection using Genetic Algorithm (AFSGA) identifies the required features automatically and reduces the computational cost in determining good clusters. The performance of AFSGA is tested using public and synthetic datasets with varying dimensionality. Experimental results have shown the improved efficacy of the algorithm with optimal clusters and computational cost.