K- Means Algorithm with Different Measurements in Clustering Approach
P.C.Chenna Reddy1, R. Siva Sankara Reddy2
1P.C.Chenna Reddy, HOD, Asso. Professor, Department of CSE, JNTU College of engineering, Pulivendula (A.P.), India.
2R. Siva Sankara Reddy, S/W Engineer, TCS, Hydarabad (A.P.), India.
Manuscript received on July 17, 2012. | Revised Manuscript received on August 20, 2012. | Manuscript published on August 30, 2012. | PP: 269-271 | Volume-1 Issue-6, August 2012.  | Retrieval Number: F0684081612/2012©BEIESP

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Abstract: Clustering techniques have been used by many intelligent software agents in order tretrieve, filter and categorize document available on the World Wide Web .Clustering is also useful in extracting salient features of related web documents to automatic ally formulate queries and search for other similar documents on the Web. In this paper, we introduce two new clustering algorithm withs K-Means Clustering in Gene Linker™ that can effectively cluster documents, even in the presence of a very high dimensional feature space. These clustering techniques, which are based on generalizations of graph partitioning, do not require prespecifiedad hoc distance functions, and are capable of automatically discovering document similarities or associations. We conduct several experiment son real Web data using various feature selection and find out the no off clusters in the data documenting this paper also discuss about the real example. In this example we are find out the no. Of clusters. 
Keywords: Clustering, Categorization, World Wide Web Documents, K-means Alogrithmg, Genelinker TM