Abstract
Web usage mining focuses on discovering the potential knowledge from the browsing patterns of users and to find the correlation between the pages on analysis. With exponential growth of web log, the conventional data mining techniques were proved to be inefficient, as they need to be re-executed every time. As web log is incremental in nature, it is necessary for web miners to use incremental mining techniques to extract the usage patterns and study the visiting characteristics of user. The data on the web log is heterogeneous and non scalable, hence we require an improved algorithm which reduces the computing cost significantly.
This paper discusses an algorithm to suit for continuously growing web log, based on association rule mining with incremental technique. The algorithm is proved to be more efficient as it avoids the generation of candidates, reduces the number of scans and allows interactive mining with different supports. To validate the efficiency of proposed algorithm, several experiments were conducted and results proven this are claimed.
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Rao, V.V.R.M., Kumari, V.V., Raju, K.V.S.V.N. (2010). Study of Visitor Behavior by Web Usage Mining. In: Das, V.V., et al. Information Processing and Management. BAIP 2010. Communications in Computer and Information Science, vol 70. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12214-9_31
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DOI: https://doi.org/10.1007/978-3-642-12214-9_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12213-2
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