Feature Selection using Genetic Algorithm for Clustering high Dimensional Data

  • Authors

    • Kahkashan Kouser
    • Amrita Priyam
    2018-04-03
    https://doi.org/10.14419/ijet.v7i2.11.11001
  • feature selection, clustering, high dimensional data, Genetic algorithm.

  • 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.

     

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    Kouser, K., & Priyam, A. (2018). Feature Selection using Genetic Algorithm for Clustering high Dimensional Data. International Journal of Engineering & Technology, 7(2.11), 27-30. https://doi.org/10.14419/ijet.v7i2.11.11001