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Authors
Affiliations
1 Department of Computer Science and Engineering, Misrimal Navajee Munoth Jain Engineering College, Anna University, IN
2 Infant Jesus College of Engineering and Technology, Anna University, Chennai, IN
Source
Data Mining and Knowledge Engineering, Vol 4, No 3 (2012), Pagination: 101-104
Abstract
Web spam is a serious problem for search engines because the quality of their results can be severely degraded by the presence of this kind of page. In this paper, we present an efficient spam detection system based on a classifier that combines new link-based features with language-model (LM)-based ones. We have specifically applied the Kullback-Leibler divergence on different combinations of these sources of information in order to characterize the relationship between two linked pages. In this paper, we present an efficient spam detection system based on a Hybrid clustering that combines K-means and SVM and then classified by using C4.5 with Qualified link-based features and Language Model(LM) based once. The result is an accurate system for detecting Web spam using fewer features.
Keywords
Content Analysis, Information Retrieval, Language Models (LMs), Link Integrity, Web Spam Detection.
