Abstract
With the development of internet, web mining has become a hotspot of data mining. The first step of web mining is to classify web pages into interesting classes, so the classification is one of the essential techniques for web mining. In this paper, we study the capabilities of bayesian classifiers for web pages categorization, after that we report our work on the comparison of binary-classification and multi-classification. Results on benchmark dataset show that bayesian classifiers perform satisfying, especially for Hidden Naive Bayes (HNB) which is more competitive than other methods. Furthermore, the performances of binary-classification are better than those of multi-classification under the evaluation metrics of accuracy and F-measure.
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