International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE)
Clustering high dimensional data results in overlapping and loss of some data. This paper extends the k-means clustering using weight function for clustering high dimensional data. The weight function can be determined by vector space model that convert high dimensional data into vector matrix. Thus the proposed algorithm is for projective clustering which is used to find the overlapping boundaries in various subspaces. The objective function is to find the relevant dimensions by reducing the irrelevant dimensions for cluster formation.