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

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Mining Association Rules from Web Logs by Incorporating Structural Knowledge of Website

by Bhawna Nigam, Suresh Jain, Sanjiv Tokekar
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 11
Year of Publication: 2012
Authors: Bhawna Nigam, Suresh Jain, Sanjiv Tokekar
10.5120/5737-7919

Bhawna Nigam, Suresh Jain, Sanjiv Tokekar . Mining Association Rules from Web Logs by Incorporating Structural Knowledge of Website. International Journal of Computer Applications. 42, 11 ( March 2012), 17-23. DOI=10.5120/5737-7919

@article{ 10.5120/5737-7919,
author = { Bhawna Nigam, Suresh Jain, Sanjiv Tokekar },
title = { Mining Association Rules from Web Logs by Incorporating Structural Knowledge of Website },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 11 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 17-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number11/5737-7919/ },
doi = { 10.5120/5737-7919 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:31:04.396100+05:30
%A Bhawna Nigam
%A Suresh Jain
%A Sanjiv Tokekar
%T Mining Association Rules from Web Logs by Incorporating Structural Knowledge of Website
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 11
%P 17-23
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

One of the basic problems with the Association Rule discovery is that when Mining Algorithms are applied on Web Access Logs, the total number of generated rules is found to be very large. For finding useful results from these rules, the analyzer needs to look into large rule-set. Moreover, the analysis of such rule-set also requires certain criteria for making decisions, i. e. a particular rule should be accepted or not. This ambiguity of acceptance or rejection of rules makes it very difficult to extract knowledge. Hence in order to get effective results with the minimized effort, number of rules should be less and valid. Therefore, the structural knowledge of Website is considered to solve the purpose, that plays an important role in pruning the invalid rules, thereby reducing the size of rule-set , and it is observed from the experiment that the number of rules have been successfully reduced.

References
  1. Jaideep Srivastava , Robert Cooleyz , Mukund Deshpande, Pang-Ning Tan : Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. ACM SIGKDD (2000).
  2. B. Santhosh Kumar, K. V. Rukmani : Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms. Int. J. of Advanced Networking and Applications 400 Volume: 01, Issue: 06, Pages: 400-404 (2010)
  3. I-Hsien Ting, Chris Kimble, Daniel Kudenko: UBB Mining: Finding Unexpected Browsing Behaviour in Clickstream Data to Improve a Web Site's Design.
  4. A. Anitha, N. Krishnan: A Web Usage Mining based Recommendation Model for Learning Management Systems. CONFERENCE- IEEE (2010)
  5. Huiping Peng:Discovery of Interesting Association Rules Based on Web Usage Mining International Conference on Multimedia Communications (2010)
  6. Mei-Ling Shyu, Choochart Haruechaiyasak, Shu-Ching Chen and Na Zhao: Collaborative Filtering by Mining Association Rules from User Access Sequences- IEEE (2005)
  7. D. Vasumathi and Dr. A Govardhan: Efficient Web Usage Mining Based on Formal Concept Analysis. Journal of Theoretical and Applied Information Technology (2005 – 2009)
  8. Faten Khalil, Jiuyong Li and Hua Wang: A Framework of Combining Markov Model with Association Rules for Predicting Web Page Accesses. Proc. Fifth Australasian Data Mining Conference (2006)
  9. Yuhua Chen, Xin Chen and Haoyi Chen: Improve on Frequent Access Path Algorithm in Web Page Personalized Recommendation Model. International Conference on Information Science and Technology Nanjing, . Jiangsu, China (March 26-28, 2011)
  10. Jos´e Lu´?s Cabral de Moura Borges: A Data Mining Model to Capture User Web Navigation Patterns. (2000)
  11. Shaofei Wu: A New Frequent Path Algorithm of Web User Access Pattern. International Conference on Industrial and Information Systems (2009)
  12. S. Taherizadeh N. Moghadam : Integrating Web Content Mining into Web Usage Mining for Finding Patterns and Predicting Users' Behavior. International Journal of Information Science and Management
  13. Daniel Mican, Nicolae Tomai : Association-Rules-Based Recommender System for Personalization in Adaptive Web-Based Applications.
  14. Maja Dimitrijevi?, Zita Bošnjak : Web Usage Association Rule Mining System. Interdisciplinary Journal of Information, Knowledge, and Management Volume 6 (2011)
  15. Web Data Mining. Net http://www. web-datamining. net/usage/
  16. Olfa Nasraoui, Esin Saka, Antonio Badia and Richard Germain: A Web Usage Mining Framework for MiningEvolving User Profiles in Dynamic Web Sites. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 20, NO. 2 (2008)
  17. Yan LI, Boqin FENG, Qinjiao MAO: Research on Path Completion Technique inWeb Usage Mining. International Symposium on Computer Science and Computational Technology (2008).
Index Terms

Computer Science
Information Sciences

Keywords

Association Rules Weblog Web Usage Mining Website Structure Trails Or Navigation Session Bfs(breadth First Search)

Powered by PhDFocusTM