Abstract
Clustering is one of the most important approaches for mining and extracting knowledge from the web. In this paper a method for clustering the web data is presented which using a Bayesian network, finds appropriate representatives for each of the clusters. Having those representatives, we can create more accurate clusters. Also the contents of the web pages are converted into vectors which firstly, the number of dimensions is reduced, and secondly the orthogonality problem is solved. Experimental results show about the high quality of the resultant clusters.
This paper is supported by Iran Telecommunication Research Center (ITRC).
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Chehreghani, M.H., Abolhassani, H. (2007). H-BayesClust: A New Hierarchical Clustering Based on Bayesian Networks. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_59
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DOI: https://doi.org/10.1007/978-3-540-73871-8_59
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