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General Information
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2012 Vol.2(5): 648-653 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2012.V2.207

Clique-Attacks Detection in Web Search Engine for Spamdexing using K-Clique Percolation Technique

S. K. Jayanthi and S. Sasikala
Abstract—Search engines make the information retrieval task easier for the users. Highly ranking position in the search engine query results brings great benefits for websites. Some website owners interpret the link architecture to improve ranks. To handle the search engine spam problems, especially link farm spam, clique identification in the network structure would help a lot. This paper proposes a novel strategy to detect the spam based on K-Clique Percolation method. Data collected from website and classified with NaiveBayes Classification algorithm. The suspicious spam sites are analyzed for clique-attacks. Observations and findings were given regarding the spam. Performance of the system seems to be good in terms of accuracy.

Index Terms—Clique, link spam, search engine, ranking, search engine optimization.

S. K. Jayanthi was with Department of Computer Science, Vellalar College for women, Erode-12, India. (e-mail: jayanthiskp@gmail.com).
S. Sasikala was with the Department of Computer Science, KSR College of Arts and Science,Tiruchengode-637211, India (e-mail:sasi_sss123 @rediff.com).


Cite:S. K. Jayanthi and S. Sasikala, "Clique-Attacks Detection in Web Search Engine for Spamdexing using K-Clique Percolation Technique," International Journal of Machine Learning and Computing vol.2, no. 5, pp. 648-653, 2012.

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