Abstract
Due to the uncertainty in accessing Web pages, analysis of Web logs faces some challenges. Several rough \(\mathnormal{k}\)-means cluster algorithms have been proposed and successfully applied to Web usage mining. However, they did not explain why rough approximations of these cluster algorithms were introduced. This paper analyzes the characteristics of the data in the boundary areas of clusters, and then a rough \(\mathnormal{k}\)-means cluster algorithm based on a reasonable rough approximation (RKMrra) is proposed. Finally RKMrra is applied to Web access logs. In the experiments RKMrra compares to Lingras and West algorithm and Peters algorithm with respect to five characteristics. The results show that RKMrra discovers meaningful clusters of Web users and its rough approximation is more reasonable.
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Miao, D., Chen, M., Wei, Z., Duan, Q. (2007). A Reasonable Rough Approximation for Clustering Web Users. In: Zhong, N., Liu, J., Yao, Y., Wu, J., Lu, S., Li, K. (eds) Web Intelligence Meets Brain Informatics. WImBI 2006. Lecture Notes in Computer Science(), vol 4845. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-77028-2_25
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DOI: https://doi.org/10.1007/978-3-540-77028-2_25
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