ANALYSIS OF CLIQUE BY MATRIX FACTORIZATION AND PARTITION METHODS
Journal Title: International Journal of Computer Science and Management Studies (IJCSMS) www.ijcsms.com - Year 2011, Vol 11, Issue 3
Abstract
In real life clustering of high dimensional data is a big problem. To find out the dense regions from increasing dimensions is one of them. We have already studied the clustering techniques of low dimensional data sets like k-means, k-mediod, BIRCH, CLARANS, CURE, DBScan, PAM etc. If a region is dense then it consists with number of data points with a minimum support of input parameter ΓΈ other wise it cannot take into clustering. So in this approach we have implemented CLIQUE to find out the clusters from multidimensional data sets. In dimension growth subspace clustering the clustering process start at single dimensional subspaces and grows upward to higher dimensional ones. It is a partition method where each dimension divided like a grid structure. In this paper the elimination of redundant objects from the regions by matrix factorization and partition method are implemented. The comparisons between CLIQUES with these two methods are studied. The redundant data point belongs to which region to form a cluster is also studied.
Authors and Affiliations
Raghunath Kar , Dr. Susant Kumar Das
Keywords
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