Abstract
Web access sequence mining discovers hidden information or knowledge from weblogs containing web usage patterns. The discovered knowledge is useful in many ways for web designers or decision makers to improve the website organization. Several algorithms have been proposed to mine web access sequence patterns and in general they generate candidate sequences and test them during the mining process. This paper describes a fast and efficient algorithm to discover web access sequences by constructing a data structure called prefix based minimized WAS-tree with maximal potential sequence patterns. The tree is recursively constructed and mined to find all the patterns in the database, satisfying the given min-sup. To prove that our algorithm is fast and efficient when compared to an existing algorithm, we have done experimental studies on a real dataset.
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References
Chen, M.S., Park, J.S., Yu, P.S.: Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering, 209–21 (1998)
Mobasher, B., Jain, N., Han, E.H.,Srivastava, J.: Web mining: Pattern discovery from World Wide Web transactions,Tech Rep: TR96-050, http://www.citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.57.4087 (last cited on 1996)
Lee, Y.S., Yen, S.J.: Incremental and interactive mining of web traversal patterns. Information Sciences 178(2), 287–306 (2008)
Pei, J., Han, J., Mortazavi-Asl, B., Zhu, H.: Mining access patterns efficiently from web logs. In: Terano, T., Chen, A.L.P. (eds.) PAKDD 2000. LNCS, vol. 1805, pp. 396–407. Springer, Heidelberg (2000)
Ezeife, C.I., Lu, Y.: Mining web log sequential patterns with position coded pre- order linked wap-tree. Data Mining and Knowledge Discovery 10(1), 5–38 (2005)
Agrawal, R., Srikant, R.: Mining Sequential Patterns. In: Proceedings ICDE 1995, pp. 3–14 (1995)
Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.C.: Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. IEEE TKDE 16, 1424–1440 (2004)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Int’l Conf Extd. DB. Tech., pp. 3–17 (1996)
Zaki, M.: SPADE: An Efficient Algorithm for Mining Frequent Sequences. Machine Lrng. 40, 31–60 (2001)
Cooley, R.: Web usage mining: discovery and application of interesting patterns from web data, Ph.D. thesis, University of Minnesota (2000)
Facca, F.M., Lanzi, P.L.: Mining interesting knowledge from weblogs: a survey. Data & Knowledge Engineering 53, 225–241 (2005)
Han, J., Kamber, M.: Data Mining Concepts and Techniques. Morgan Kaufmann, San Francisco (2001)
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Thilagu, M., Nadarajan, R. (2011). Fast and Efficient Mining of Web Access Sequences Using Prefix Based Minimized Trees. In: Abraham, A., Lloret Mauri, J., Buford, J.F., Suzuki, J., Thampi, S.M. (eds) Advances in Computing and Communications. ACC 2011. Communications in Computer and Information Science, vol 191. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22714-1_38
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DOI: https://doi.org/10.1007/978-3-642-22714-1_38
Publisher Name: Springer, Berlin, Heidelberg
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