CFP last date
20 June 2024
Reseach Article

Web Usage Mining: An Approach

by Pooja
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 12
Year of Publication: 2014
Authors: Pooja

Pooja . Web Usage Mining: An Approach. International Journal of Computer Applications. 86, 12 ( January 2014), 39-42. DOI=10.5120/15041-3387

@article{ 10.5120/15041-3387,
author = { Pooja },
title = { Web Usage Mining: An Approach },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 12 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { },
doi = { 10.5120/15041-3387 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-06T22:04:05.870657+05:30
%A Pooja
%T Web Usage Mining: An Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 12
%P 39-42
%D 2014
%I Foundation of Computer Science (FCS), NY, USA

PL-WAP tree mining algorithm was a great improvement over the WAP tree algorithm which was earlier used for mining in the server web logs. This was basically done to find out frequent sequence patterns. In WAP-tree mining, we used to engage in continuous reconstruction of intermediate WAP trees for each level of recursion to extract frequent patterns. In the WAP technique we could not formulate a method by which we could infer which sequences would be the suffix sequences of a given last event. This was the major drawback of the algorithm. In order to solve this problem, binary codes were introduced to uniquely represent the exact position of each and every node in the WAP-tree. Whereas PLWAP tree is not efficient when the server log data gets frequently updated. Therefore incremental data mining approach is needed to handle this problem. PL4UP proposed later is an incremental mining methodology added to the PLWAP. But the numerous parameters considered, sets generated, insertions and deletions performed at each stage makes it less efficient. and it works efficiently only when the percentage updation in size is less than fifty. In this paper, a novel method is proposedto efficiently handle the updations right from the pre-processing stage and the key factors in the implementation of the method are described. A web usage mining tool was also developed to experimentally validate the method.

  1. Brijendra Singh, Hemant Kumar Singh;"Web Data Mining Research: A Survey", 978-1-4244-5967-4/10/$26. 00 ©IEEE, 2010.
  2. Sang T. T. Nguyen;"Efficient Web Usage Mining Process for Sequential Patterns", Proceedings of iiWAS,2009.
  3. Ezeife, C. I. , and Lu, Y. "Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree". Data Mining and Knowledge Discovery. 10, 1, 5-38. DOI=10. 1007/s10618-005-0248-3, 2005.
  4. R. Agrawal and R. Srikant, "Mining sequential patterns," In: Proceedings of the 11th Int'l conference on data engineering, pp 3–14, Taipei, 1995.
  5. Masseglia, F. , Poncelet, P. , and Cicchetti, R. ,"An efficient algorithm for web usage mining. Networking and Information Systems Journal (NIS)", 2(5/6):571–603,1999.
  6. Nanopoulos, A. and Manolopoulos, Y. , "Mining patterns from graph traversals. Data and Knowledge Engineering", 37(3):243–266, 2001.
  7. Han, J. and Kamber, M. , Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, 2000.
  8. Pei, J. , Han, J. , Mortazavi-Asl, B. , and Zhu, H. ,"Mining access patterns efficiently from web logs". In Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'00). Kyoto, Japan, 2000.
  9. Han, J. , Pei, J. , Yin, Y. , and Mao, R, "Mining frequent patterns without candidate generation: A frequent pattern tree approach", International Journal of Data Mining and Knowledge Discovery. Kluwer Academic Publishers, 8(1): 53–87, 2004.
  10. Borges, J. and Levene, M. ,"Data mining of user navigation patterns". In Masand, B. and Spliliopoulou, M. , editors, Web Usage Analysis and User Proling, Lecture Notes in Artificial Intelligence (LNAI 1836), pages 92{111. Springer Verlag, Berlin, 2000.
  11. Liu, Y. , Huang, X. , and An, A. "Personalized Recommendation with Adaptive Mixture of Markov Models". The American Society for Information Science and Technology. 58, 12, 1851–1870. DOI=10. 1002/asi. 20631, 2007.
Index Terms

Computer Science
Information Sciences


Web mining incremental data mining frequent nodes