Research Article | OPEN ACCESS
Efficient Clustering of Web Search Results Using Enhanced Lingo Algorithm
1M. Manikantan and 2S. Duraisamy
1Department of Computer Applications, Kumaraguru College of Technology
2Department of Computer Applications, Sri Krishna College of Engineering and Technology, Coimbatore, India
Research Journal of Applied Sciences, Engineering and Technology 2015 5:359-364
Received: September 18, 2014 | Accepted: October 01, 2014 | Published: February 15, 2015
Abstract
Web query optimization is the focus of recent research and development efforts. To fetch the required information, the users are using search engines and sometimes through the website interfaces. One approach is search engine optimization which is used by the website developers to popularize their website through the search engine results. Clustering is a main task of explorative data mining process and a common technique for grouping the web search results into a different category based on the specific web contents. A clustering search engine called Lingo used only snippets to cluster the documents. Though this method takes less time to cluster the documents, it could not be able to produce the clusters of good quality. This study focuses on clustering all documents using by applying semantic similarity between words and then by applying modified lingo algorithm in less time and produce good quality.
Keywords:
Clustering algorithm, lingo algorithm, search engine optimization, semantic web, snippet, web query optimization,
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Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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