Abstract
One important problem proposed recently in the field of web mining is website classification problem. The complexity together with the necessity to have accurate and fast algorithms yield to many attempts in this field, but there is a long way to solve these problems efficiently, yet. The importance of the problem encouraged us to work on a new approach as a solution. We use the content of web pages together with the link structure between them to improve the accuracy of results. In this work we use Naïve-bayes models for each predefined webpage class and an extended version of Hidden Markov Model is used as website class models. A few sample websites are adopted as seeds to calculate models’ parameters. For classifying the websites we represent them with tree structures and we modify the Viterbi algorithm to evaluate the probability of generating these tree structures by every website model. Because of the large amount of pages in a website, we use a sampling technique that not only reduces the running time of the algorithm but also improves the accuracy of the classification process. At the end of this paper, we provide some experimental results which show the performance of our algorithm compared to the previous ones.
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Yazdani, M., Eftekhar, M., Abolhassani, H. (2009). Tree-Based Method for Classifying Websites Using Extended Hidden Markov Models. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_80
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DOI: https://doi.org/10.1007/978-3-642-01307-2_80
Publisher Name: Springer, Berlin, Heidelberg
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