Open AccessFeature Selection using Genetic Algorithm for Clustering high Dimensional Data
Author(s) -
K. Kouser,
Amrita Priyam
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i2.11.11001
Subject(s) - cluster analysis , feature (linguistics) , pattern recognition (psychology) , feature selection , clustering high dimensional data , algorithm , subspace topology , feature vector , genetic algorithm , set (abstract data type) , computer science , data mining , data set , canopy clustering algorithm , correlation clustering , cure data clustering algorithm , artificial intelligence , mathematics , machine learning , programming language , philosophy , linguistics
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. Finally, the result of GA-HDClustering is compared with the traditional clustering algorithm. For comparison different validity matrices such as Sum of squared error (SSE), Within Group average distance (WGAD), Between group distance (BGD), Davies-Bouldin index(DBI), 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.