Abstract
Edit distance, as a similarity measure between user traversal patterns, satisfies the need of varying-length of user traversal sequences very well because it can be computed between different-length symbol strings which needs lower time and storage expense. Moreover, web topology is skillfully used to compute the relationship between pages which is used as a measure of cost of an edit operation. Finally, two-threshold sequential clustering method (TTSCM) is used to cluster user traversal patterns avoiding specifying the number of cluster in advance, and reducing the dependency between the clustering results and the clustering order of traversal patterns. Experimental results test and verify the effectiveness and flexibility of our proposed methods.
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References
Fu, Y., Sandhu, K., Shih, M.-Y.: A Generalization-Based Approach to Clustering of Web Usage Sessions. In: The International Workshop on Web Usage Analysis and User Profiling, pp. 21–38 (1999)
Lin, W., Liu, Y., Zhu, Q., Xi, D.: Web User Clustering and Personalized Recommendation Based on Mixture of Hidden Markov Chain Models. Journal of the China Society for Scientific and Technical Information 28(4), 557–564 (2009)
Jia, L.v.: Web Log Mining Based upon Immune Clustering. Journel of Chongqing Normal University (Natural Science Edition) 24(3), 32–35 (2007)
Li, S.S., Fang, S.H.: Improved clustering algorithm based on web usage mining technology. Computer Engineering and Design 30(22), 5182–5184 (2009)
Levenshtein, V.I.: Binary Codes Capable of Correcting Deletions, Insertions and Reversals. Doklady Akademii Nauk SSSR 163(4), 845–848 (1965)
Bellman, R.: Dynamic Programming. Princeton Univ. Press, Princeton (1957)
Marzal, A., Vidal, E.: Computation of Normalized Edit Distance and Applications. IEEE Trans. Pattern Analysis and Machine Intelligence 15(9), 926–932 (1993)
Oncina, J., Sebban, M.: Learning stochastic edit distance: Application in handwritten character recognition. Pattern Recognition 39, 1575–1587 (2006)
Saigo, H., Vert, J.-p., Ueda, N., Akutsu, T.: Protein homology detection using string alignment kernelsEddy. Bioinformatics (February 2004)
Depaul CTI Web Usage Mining Data, http://maya.cs.depaul.edu/~classes/etc584/resource.html
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Tan, X., Xu, M. (2011). A Novel Approach to Cluster Web Traversal Patterns Based on Edit Distance. In: Zhiguo, G., Luo, X., Chen, J., Wang, F.L., Lei, J. (eds) Emerging Research in Web Information Systems and Mining. WISM 2011. Communications in Computer and Information Science, vol 238. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24273-1_60
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DOI: https://doi.org/10.1007/978-3-642-24273-1_60
Publisher Name: Springer, Berlin, Heidelberg
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