Frequent Navigation Pattern Mining from Web usage data
Keywords:
Frequent Pattern Mining, Apriori Algorithm, Web Usage Mining, User Session GenerationAbstract
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.
References
R. Agrawal, R. Srikant, “Mining sequential patterns”, In ICDE, USA, pp.–14, 1995.
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.
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.
S. Brohee, J.V. Helden, “ Evaluation of clustering algorithms for protein-protein interaction networks”, BMC bioinformatics, Vol.7, Issue.1, p.488-496, 2006.
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.
D. Chakrabart, C. Faloutsos, “Graph mining: Laws, generators, and algorithms”, ACM Computer Survey, Vol.38, Issue.1, pp.204-212, 2006.
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.
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.
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.
J. Dean, S. Ghemawat. “Map reduce: Simplified data processing on large clusters”, In OSDI, USA, pp.137–150, 2004.
J. Han, J. Pei, M. Kamber, "Data mining concept and techniques ", Elsevier, Netherlands, pp.1-744, 2011.
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.
B. Mobasher, N. Jain, E. Han, J. Srivastava, “Web Mining: Pattern discovery from World”, University of Minnesota, USA, pp.1-12, 1996.
Wide Web transactions. Technical Report TR 96-050, Department of Computer Science, University of Minnesota, Minneapolis, pp.1-25, 1996.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
