Abstract With the rapid growth of information on the World Wide Web (WWW), classification of web documents has become important for efficient information retrieval. Relevancy of information retrieved can also be improved by considering semantic relatedness between words which is a basic research area in fields of natural language processing, intelligent retrieval, document clustering and classification, word sense disambiguation etc. The web search engine based semantic relationship from huge web corpus can improve classification of documents. This paper proposes an approach for web document classification that exploits information, including both page count and snippets. To identify the semantic relations between the query words, a lexical pattern extraction algorithm is applied on snippets. A sequential pattern clustering algorithm is used to form clusters of different patterns. The page count based measures are combined with the clustered patterns to define the features extracted from the word-pairs. These features are used to train the Support Vector Machine (SVM), in order to classify the web documents. Experimental results demonstrate 5% and 9% improvement in F1 measure for Reuters 21578 and 20 Newsgroup datasets in the classifier performance.

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[23] Yang J. and Watada J., Decomposition of Term- Document Matrix Representation for Clustering Analysis, in Proceeding of International Conference of Fuzzy Systems, Taipei, pp.976- 983, 2011. Kavitha Chinniyan is working as an Assistant Professor (Senior Grade) in Department of Computer Science and Engineering in PSG College of Technology, India. She is pursuing her research work in Semantics in Large Scale Distributed Systems. Her area of interests includes semantic web technology, parallel processing and data structures. She has published 5 papers in referred Journals and 4 papers in Conferences. Sudha Gangadharan is working as a professor in CSE Department of PSG College of Technology. She has 20 years of teaching experience. Her area of interest includes distributed systems and software engineering. She has published 5 books, 30 papers in referred Journals and 32 papers in National and International Conferences. She has coordinated two AICTE-RPS projects in the areas of distributed computing. She is the coordinator of PSG-Yahoo research in grid and cloud computing, Nokia Research on Big Data Analytics and Xurmo Research in social networking. Kiruthika Sabanaikam is a Post Graduate student of ME-Software Engineering in Department of Computer Science and Engineering in PSG College of Technology, India. Her area of interest is data mining and semantic web technology.