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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 9    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 9, September 2011
A Study Of Web Navigation Pattern Using Clustering Algorithm In Web Log Files
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Mrs.V.Sujatha, Dr.Punithavalli
Classification, Clustering, Web mining, Weblog data, and Web usage mining.
Web user navigation pattern is a heavily researched area in the field of web usage mining with wide range of applications. Web usage mining is the process of applying data mining techniques to the discovery of usage pattern from data extracted from web log files. Discovering hidden information from Web log data is called Web usage mining. The aim of discovering frequent patterns in Web log data is to obtain information about the navigational behavior of the users. This can be used for advertising purposes, for creating dynamic user profiles etc. In this paper four types of clustering approaches are investigated in web log files to improve the quality of clustering for user navigation pattern in web usage mining systems, for predicting user's intuition in the large web sites.
[1] B. Mobasher, R. Cooley, and J. Srivastava, ""Automatic personalization based on Web usage mining,"" Communications of the ACM, vol. 43, pp. 142-151, 2000.

[2] F. Masseglia, P. Poncelet, and R. Cicchetti, An Efficient Algorithm for Web Usage Mining, Networking and Information Systems Journal (NIS), 2(5-6), pp. 571-603, 1999.

[3] R. Cooley, Web Usage Mining: Discovery and Application of Interesting patterns from Web Data, Ph. D. Thesis, University of Minnesota, Department of Computer Science, 2000.

[4] P. Pirolli, J. Pitkow, and R. Rao, Silk From a Sow‘s Ear: Extracting Usable Structures from the Web, Proceeding on Human Factors in Computing Systems (CHI’96), ACM Press, pp. 118-125, 1996.

[5] M. Spiliopoulou, and L.C. Faulstich, WUM: A Web Utilization Miner,proceeding of EDBT Workshop on the Web and Data Bases (WebDB’98), Springer Verlag, pp. 109-115, 1999.

[6] J. Srivastava, R. Cooley, M. Deshpande, and P.N. Tan, Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data, SIGKDD Explorations, 1(2), pp. 12-23, 2000.

[7] F. Masseglia , P. Poncelet, M. Teisseire, A. Marascu, Web usage mining: extracting unexpected periods from web logs, Data Min Knowl Disc, 16, pp.39–65, 2008.

[8] M. Spiliopoulou , L.C. Faulstich , K. Winkler , A data miner analyzing the navigational behavior of web users, Proceeding of the workshop on machine learning in user modeling of the ACAI’99 international Conference Creta, Greece, 1999.

[9] F. Bonchi , F. Giannotti , C. Gozzi , G. Manco, M. Nanni , D. Pedreschi, C. Renso , S. Ruggieri, Web log data warehousing and mining for intelligent web caching , Data Knowl Eng, 39(2), pp. 165– 189, 2001.

[10] B. Hay , G. Wets, K. Vanhoof , Mining navigation patterns using a Sequence alignment method , Knowl Inf Syst, 6(2), pp.150–163, 2004.

[11] Zhu, J., Hong, J., Hughes, J.G. 2002 Using Markov chains for link Prediction in adaptive web sites. Proceeding of soft-ware: first International conference on computing in an imperfect world, Belfast, UK, pp. 60–73, 2002.

[12] M. Nakagawa, B. Mobasher, Impact of site characteristics on recommendation models based on association rules and sequential patterns. Proceeding of the IJCAI’03 workshop on intelligent techniques for web personalization, Mexico, 2003.

[13] R. Srikant, R. Agrawal, Mining sequential patterns: generalizations and performance improvements. Proceeding of the 5th international conference on extending database technology (EDBT’96), pp. 3–17, France, 1996.

[14] A. Mueller, Fast sequential and parallel algorithms for association rules mining: a comparison, Technical report CS-TR-3515, Department of Computer Science, University of Maryland- College Park, 1995.

[15] A. Abraham, V. Ramos, Web Usage Mining Using Artificial Ant Colony Clustering and Genetic Programming, Congress on Evolutionary Computation (CEC), IEEE 2003.

[16] W3C extended log file format. Available at http://www.w3.org/TR/WD-logfile

[17] WCA. Web characterization terminology & definitions. Available at http://www.w3.org/1999/05/WCA-terms/ .

[18] M. Eirinaki, M.Vazirgiannis, Web Mining for Web Personalization, Athens University of Economics and Business, 2003.

[19] J. Huysmans, B. Baesens , J. Vanthienen , Web Usage Mining: A Practical Study, Katholieke Universities Leuven, Dept. of Applied Economic Sciences, 2003.

[20] RFC 1413. Identification Protocol. Available at http://www.rfceditor.org/rfc/rfc1413.txt .

[21] L. Catledge, J. Pitkow, Characterizing browsing behaviors on the World Wide Web, Computer Networks and ISDN Systems, 27(6),1999.

[22] J. Deneubourg -L., S. Goss, N. Franks, A. Sendova-Franks, C. Detrain, L. Chrétien, The dynamics of collective sorting: robot-like ants and ant-like robots. Proceeding of the first international conference on simulation of adaptive behavior, pp. 356–365, MIT Press, 1991.

[23] J. Handl, B. Meyer, Ant-based and Swarm-based clustering, Swarm Intelligence, 1, pp. 95–113, 2007.

[24] E. Lumer, B. Faieta, Diversity and adaptation in populations of clustering ants. Proceeding of the third international conference on Simulation of adaptive behaviour, pp. 501–508, MIT Press, 1994.

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