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All clustering methods have to assume some cluster relationship on the list of data objects that they really are applied on. GraphBased Document Clustering works with frequent sense s rather than frequent keywords used in traditional text mining techniques.Similarity between a pair of objects can be defined either explicitly or implicitly. With tpaper, we analyzed existing multi-viewpoint based similarity measure and two re lated clustering methods. The main difference betwe en a traditional dissimilarity/similarity measure and ours could be that the former uses merely a single viewpoint, whi ch is the origin, even though the latter utilizes many viewpoints, which you ll find are objects assumed to not have the very same clust er using the two objects being measured. Using multiple viewpoints, more informati ve assessment of similarity could well be achieved. Theoretical analysis and empirical study are conducted to back up this claim . Two criterion functions for document clustering a re proposed dependent on this wonderful measure. We compare them several well-kno wn clustering algorithms which use other popular si milarity measures on various document collections confirming the good si des of our proposal.

HIERARCHAL CLUSTERING AND SIMILARITY MEASURES ALONG WITH MULTI REPRESENTATION