Frequent Navigation Pattern Mining from Web usage data

V. Jain¹

Section:Research Paper, Product Type: Isroset-Journal
Vol.1 , Issue.1 , pp.47-51, Jan-2013

Online published on Dec 12, 2013

Copyright © V. Jain . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

View this paper at   Google Scholar | DPI Digital Library

XML View     PDF Download

How to Cite this Paper

Abstract :
Web usage mining provides the information about the user and their behavioural aspects of the web navigation. Traditional frequent sequence pattern mining algorithms are limited in analyzing information from big datasets. However, a graph based approach with the efficient version of apriori algorithm can generate frequent patterns from large datasets. In our work, we have implemented a web graph approach for generating user sessions and apriori all algorithm for generating frequent patterns.

Key-Words / Index Term :
Frequent Pattern Mining, Apriori Algorithm, Web Usage Mining, User Session Generation

References :
[1] R. Agrawal, R. Srikant, “Mining sequential patterns”, In ICDE, USA, pp.–14, 1995.
[2] M. A. Bayir, D. Guney, T. Can, “Integration of topological measures for eliminating non-specific interactions in protein interaction networks”, Discrete Applied Mathematics, Vol.157, Issue10, pp.2416–2424, 2008.
[3] J. Borges, M. Levene, “Generating dynamic higher-order markov models in web usage mining”, PKDD`05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases, UK, pp.34-45, 2005.
[4] S. Brohee, J.V. Helden, “ Evaluation of clustering algorithms for protein-protein interaction networks”, BMC bioinformatics, Vol.7, Issue.1, p.488-496, 2006.
[5] L. D. Cat ledge, J. E. Pitkow. Characterizing browsing strategies in the world-wide web. Computer Networks and ISDN Systems, Vol.27, Issue.6, pp.1065–1073, 1995.
[6] D. Chakrabart, C. Faloutsos, “Graph mining: Laws, generators, and algorithms”, ACM Computer Survey, Vol.38, Issue.1, pp.204-212, 2006.
[7] R. Cooley, B. Mobasher, J. Srivastava, "Web mining: information and pattern discovery on the World Wide Web”, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, CA, pp. 558-567, 1997.
[8] R. Cooley, B. Mobasher, J. Srivastava, “Data preparation for mining world wide web browsing patterns”, Knowl. Inf. Syst., Vol.1, Issue.1, pp.5–32, 1999.
[9] R. Cooley, P.-N. Tan,J. Srivastava, “Discovery of interesting usage patterns from web data”, Proceeding Web-based Knowledge Discovery and Data Mining WEBKDD`99, London, pp. 163–182, 1999.
[10] J. Dean, S. Ghemawat. “Map reduce: Simplified data processing on large clusters”, In OSDI, USA, pp.137–150, 2004.
[11]J. Han, J. Pei, M. Kamber, "Data mining concept and techniques ", Elsevier, Netherlands, pp.1-744, 2011.
[12]D. Donato, L. Laura, S. Leonardi, S. Millozzi, “The web as a graph: How far we are”, ACM Transaction Internet Technology, Vol.7, Issue.1, pp. 128-136, 2007.
[13] B. Mobasher, N. Jain, E. Han, J. Srivastava, “Web Mining: Pattern discovery from World”, University of Minnesota, USA, pp.1-12, 1996.
[14]Wide Web transactions. Technical Report TR 96-050, Department of Computer Science, University of Minnesota, Minneapolis, pp.1-25, 1996.