Feature Selection using Genetic Algorithm for Clustering high Dimensional Data - Kouser, Kahkashan; Priyam, Amrita (International Journal of Engineering and Technology, 2018)
One of the open problems of modern data mining is clustering high dimensional data. For this in the paper a new technique called GA-HDClustering is proposed, which works in two steps. First a GA-based feature selection algorithm is designed to determine the optimal feature subset; an optimal feature subset is consisting of important features of the entire data set next, a K-means algorithm is applied using the optimal feature subset to find the clusters. On the other hand, traditional K-means algorithm is applied on the full dimensional feature space.Â Â Â Finally, the result of GA-HDClusteringÂ is Â comparedÂ withÂ the Â traditional Â clusteringÂ algorithm.Â For comparison different validity Â matrices Â such Â as Â SumÂ of Â squared Â error Â (SSE), Â Within Â Group average distance (WGAD), Between group distance (BGD), Davies-Bouldin index(DBI),Â Â are used .The GA-HDClustering uses genetic algorithm for searching an effective feature subspace in a large feature space. This large feature space is made of all dimensions of the data set. The experiment performed on the standard data set revealed that the GA-HDClustering is superior to traditional clustering algorithm.Â