CFP last date
20 June 2024
Call for Paper
July Edition
IJCA solicits high quality original research papers for the upcoming July edition of the journal. The last date of research paper submission is 20 June 2024

Submit your paper
Know more
Reseach Article

Web Log based Analysis of User's Browsing Behavior

by Ashwini Ladekar, Pooja Pawar, Dhanashree Raikar, Jayshree Chaudhari
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 11
Year of Publication: 2015
Authors: Ashwini Ladekar, Pooja Pawar, Dhanashree Raikar, Jayshree Chaudhari
10.5120/20193-2430

Ashwini Ladekar, Pooja Pawar, Dhanashree Raikar, Jayshree Chaudhari . Web Log based Analysis of User's Browsing Behavior. International Journal of Computer Applications. 115, 11 ( April 2015), 5-8. DOI=10.5120/20193-2430

@article{ 10.5120/20193-2430,
author = { Ashwini Ladekar, Pooja Pawar, Dhanashree Raikar, Jayshree Chaudhari },
title = { Web Log based Analysis of User's Browsing Behavior },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 11 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 5-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number11/20193-2430/ },
doi = { 10.5120/20193-2430 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:54:31.806833+05:30
%A Ashwini Ladekar
%A Pooja Pawar
%A Dhanashree Raikar
%A Jayshree Chaudhari
%T Web Log based Analysis of User's Browsing Behavior
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 11
%P 5-8
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In The increasing craze of internet has geared a number of modern firms for using web technology in their day to day lives. A remarkable ability to analyze web log data is provided to them by the web mining technology, that are putatively full of information, but frequently lacking with meaningful information. This signifies the need to the development of an inference mechanism which is advanced and that draws out richer implication from mining's output. This paper presents a web mining algorithm that aims at amending the interpretations of the draft's output of association rule mining. This algorithm is being tremendously used in web mining. The results obtained prove robustness of the algorithm proposed in this paper.

References
  1. B. Hay, G. Wets, K. Vanhoof. Segmentation of visiting patterns on Web sites using a sequence alignment method. Journal of Retailing and Consumer Services, 2003, 10 (3) :145–153.
  2. K. A. Smith, A. Ng. Web page clustering using a self-organizing map of user navigation patterns. Decision Support Systems, 2003, 35 (2): 245–256.
  3. J. D. Martin-Guerrero, A. Palomares, E. Balaguer-Ballster, E. Soria-Olivas, J. Gomez-Sanchis, A. Soriano-Asensi. Studying the feasibility of a recommender in a citizen Web portal based on user modeling and clustering algorithm. Expert Systems with Applications, 2006, 30 (2) :299–312.
  4. S. K. Rangarajan, V. V. Phoha, K. S. Balagani, R. R. Selmic, S. S. Iyengar. Adaptive neural network clustering of Web users. IEEE Computer, 2004, 37 (4) :34–40.
  5. R. J. Kuo, J. L. Liao, C. Tu. Integration of ART2 neural network and genetic K-means algorithm for analyzing Web browsing paths in electronic commerce. Decision Support Systems, 2005, 40 (2) :355–374.
  6. C. Shahabi, F. Banaei-Kashani. Efficient and anonymous web-usage mining for Web personalization. Journal on Computing, 200315 (2):123–147.
  7. Q. Yang, J. Z. Huang, M. Ng. A data cube model for prediction-based Web prefetching. Journal of Intelligent Information Systems, 2003, 20(1) :11–30.
  8. F. M. Facca, P. L. Lanzi. Mining interesting knowledge from weblogs: a survey. Data and Knowledge Engineering, 2005, 53 (3):225–241.
  9. G. Paliouras, C. Papatheodorou, V. Karkaletsis, C. D. Spyropoulos, V. Malaveta. Learning user communities for improving the services of information providers. 1998, Comput. Sci, 1513: 367–384.
  10. J. S. Park, M. S. Chen, P. S. Yu. Using a hash-based method with transaction trimming for mining association rules. IEEE Trans. Knowledge Data Engng, 1997, 9 (5): 813–825.
  11. M. S. Chen, J. S. Park, P. S. Yu. Efficient data mining for path traversal patterns. IEEE Trans. Knowledge Data Engng, 1998, 10 (2) :209–221.
  12. J. Borges, M. Levene. Data mining of user navigation patterns, in: Web Usage Analysis and User Profiling, Lecture Notes in Computer Science, Springer, Berlin, 2000, 1836: 92–111.
  13. Kuo, R. J. , Chen, J. H. , Hwang, Y. C. An intelligent stock trading decision support system through integration of genetic algorithm based fuzzy neural network and artificial neural network[J]. Fuzzy Sets and Systems, 2001, 118(1):21–45.
  14. Weigen, A. S. , Rumelhart, D. E. Generalization by weight-elimination with application to forecasting. Advances in Neural Information Processing Systems[J]. 1999, 3:875–882.
  15. Chen, M, S, Han, J. Data mining: an overview from a database perspective[J]. IEEE Transactions on Knowledge and Data Engineering, 2006, 8(6): 866–883.
  16. Schafer, J. B. , Konstan. E-commerce recommendation application[J]. Journal of Data Mining and Knowledge Discovery, 2001, 16:125–153.
  17. Giudici, P, Passerone, G. Data mining of association structures to model e-shopper behavior. Computational Statistics and Data Analysis[J]. 2002, 38:533–541.
  18. P. Kumar, R. S. Bapi, P. R. Krishna. A sequence clustering algorithm for Web personalization, International Journal of Data Warehousing and Mining, 2007, 3 (1):29–53.
  19. P. Kumar, P. R. Krishna, R. S. Bapi, S. K. De. Rough clustering of sequential data. Data and Knowledge Engineering, 2007, 63 (2) 183–199.
  20. R. Sen, M. H. Hansen. Predicting Web users next access based on log data. Journal of Computational and Graphical Statistics, 2003, 12:143–155.
  21. S. Park, N. C. Suresh. Performance of fuzzy ART neural network and hierarchical clustering for part-machine grouping based on operation sequences. International Journal of Production Research, 2003, 41 (14) :3185–3216.
  22. Changchien, S. Mining association rules procedures to support on-line recommendation by e-shoppers and products fragmentation [J]. Expert Systems with Applications, 2001, 20(4):325–335.
  23. Song, H, Kim, J. Mining the change of e-shopper behavior in an Internet shopping mall[J]. Expert System with Applications, 2001, 21(3):157–168.
  24. Anand, S, Patrick, A. A data mining methodology for cross-sales[J]. Knowledge-Based Systems, 2006, 10:449-461.
Index Terms

Computer Science
Information Sciences

Keywords

Page Interest Estimation Web log data mining Apriori algorithm.