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Machine Learning Techniques in Web Content Mining: A Comparative Analysis

Basavaraj S. Anami

KLE Institute of Technology, HUBLI, India

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Ramesh S. Wadawadagi

Basaveshwar Engineering College, BAGALKOT, India

Corresponding author.

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Veerappa B. Pagi

Basaveshwar Engineering College, BAGALKOT, India

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https://doi.org/10.1142/S0219649214500051Cited by:9 (Source: Crossref)

Abstract

With incessantly growing amount of information published over Web pages, the World Wide Web (WWW) has become prolific in the field of data mining research. The heterogeneous and semi-structured nature of Web data has made the process of automated discovery a challenging issue. Web Content Mining (WCM) essentially uses data mining techniques to effectively discover knowledge from Web page contents. The intent of this study is to provide a comparative analysis of Machine Learning (ML) techniques available in the literature for WCM. For analysis, the article focuses on issues such as representation techniques, learning methods, datasets used and performance of each method as a criterion. The survey observes that some of the traditional ML algorithms have been efficiently used to work on Web data. Finally, the paper concludes citing some promising issues for further research in this domain.

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