TITLE

Comparative Analysis for Alignment Based Document Clustering

AUTHOR(S)
Veeraman, T.; Nedunchelian, R.
PUB. DATE
June 2014
SOURCE
Australian Journal of Basic & Applied Sciences;Jun2014, Vol. 8 Issue 9, p22
SOURCE TYPE
Academic Journal
DOC. TYPE
Article
ABSTRACT
Background: Document Clustering is a technique that organizes a large quantity of unordered text Document into small number of meaning full and coherent cluster. Clustering approach facilitates the presentation of search result in more compact form and enables thematic browsing result set. Objective: The main problem of existing web search result based on poor vector representation of snippets. The Data units returned from the underlying database are normally encoded into the result page dynamically for human browsing which essential for many application such as internet comparison, shopping, and also be extracted out and assigned meaningful labels. Result: We present a clustering approach such K-Means, Weighted K-Means and Enhanced K-Means Algorithm. This method is capable of handling a variety of clustering approach based on Alignment Algorithm. Conclusion: Our Experimental result shows that the precision and result are achieved to improve the performance of clustering system is highly effective.
ACCESSION #
97368396

 

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