Abstract
Grouping web transactions into clusters is important in order to obtain better understanding of user’s behavior. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. However, the processing time is still an issue due to the high complexity for finding the similarity of upper approximations of a transaction which used to merge between two or more clusters. On the other hand, the problem of more than one transaction under given threshold is not addressed. In this paper, we propose an alternative technique for grouping web transactions using rough set theory. It is based on the two similarity classes which are nonvoid intersection.
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Yanto, I.T.R., Herawan, T., Deris, M.M. (2010). A Framework of Rough Clustering for Web Transactions. In: Nguyen, N.T., Katarzyniak, R., Chen, SM. (eds) Advances in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 283. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12090-9_23
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DOI: https://doi.org/10.1007/978-3-642-12090-9_23
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