Title: Optimised K-means for web search

Authors: S. Poomagal; T. Hamsapriya

Addresses: Department of Computer and Information Sciences, PSG College of Technology, Peelamedu, Coimbatore – 641004, India. ' Department of Information Technology, PSG College of Technology, Peelamedu, Coimbatore – 641004, India

Abstract: With the vast amount of information available online, searching documents relevant to a given query requires the user to go through many titles and snippets. This searching time can be reduced by grouping search results into clusters so that the user can select the relevant cluster at a glance by looking at the cluster labels. A new method of search results clustering is introduced in this paper which clusters the search results using optimised K-means algorithm using the terms from URL, title tag and meta tag as features. Optimisation of K-means algorithm is done by selecting the initial centroids using scale factor method. The proposed method of clustering is compared with existing snippet clustering algorithms in terms of intra-cluster distance and inter-cluster distance. Results show that the proposed method produces high quality clusters than the existing methods.

Keywords: search engines; stemming; snippets; search results clustering; centroids; queries; intra-cluster distance; inter-cluster distance; web search; optimised K-means; information retrieval.

DOI: 10.1504/IJAIP.2012.048142

International Journal of Advanced Intelligence Paradigms, 2012 Vol.4 No.2, pp.132 - 148

Published online: 23 Aug 2014 *

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