The week's pick
Random Articles
Reseach Article
An Efficient Hierarchical Frequent Pattern Analysis Approach for Web Usage Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 43 - Number 15 |
| Year of Publication: 2012 |
| Authors: G. Sudhamathy, C. Jothi Venkateswaran |
10.5120/6176-8603
|
G. Sudhamathy, C. Jothi Venkateswaran . An Efficient Hierarchical Frequent Pattern Analysis Approach for Web Usage Mining. International Journal of Computer Applications. 43, 15 ( April 2012), 1-7. DOI=10.5120/6176-8603
Abstract
Web usage mining aims to discover interesting user access patterns from web logs. Web usage mining has become very critical for effective web site management, creating adaptive web sites, business and support services, personalization and so on. In this paper, an efficient approach for frequent pattern mining using web logs for web usage mining is proposed and this approach is called as HFPA. In this approach HFPA, the proposed technique is applied to mine association rules from web logs using normal Apriori algorithm, but with few adaptations for improving the interestingness of the rules produced and for applicability for web usage mining. This technique is applied and its performance is compared with that of classical Apriori-mined rules. The results indicate that the proposed approach HFPA not only generates far fewer rules than Apriori-based algorithms (FPA), the generated rules are also of comparable quality with respect to three objective performance measures, Confidence, Lift and Conviction. Association mining often produces large collections of association rules that are difficult to understand and put into action. In this paper effective pruning techniques are proposed that are characterized by the natural web link structures. Experiments showed that interestingness measures can successfully be used to sort the discovered association rules after the pruning method was applied. Most of the rules that ranked highly according to the interestingness measures proved to be truly valuable to a web site administrator.
References
- Kannan, S. , & Bhaskaran, R. (2009) Association rule pruning based on interestingness measures with clus-tering. International Journal of Computer Science Issues, IJCSI, 6(1), 35-43.
- Liqiang Geng and Howard J. Hamilton, "Interestingness Measures for Data Mining: A Survey", ACM Computing Surveys, Vol. 38, No. 3, Article 9, September 2006.
- P. Tan, V. Kumar, and J. Srivastava. "Selecting the Right Interestingness Measure for Association Patterns". Technical Report 2002-112, Army High Performance Computing Research Center, 2002.
- Liaquat Majeed Sheikh, Basit Tanveer, Syed Mustafa Ali Hamdani. "Interesting Measures for Mining Association Rules", In Proceedings of INMIC 2004. 8th international Multitopic Conference, 2004, pp 641-644.
- Tianyi Wu, Yuguo Chen, and Jiawei Han, "Association Mining in Large Databases: A Re-Examination of Its Measures", In Proceedings of PKDD-2007, 11th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Warsaw, Poland, September 17-21, 2007, pp 621-628.
- R. Iváncsy and I. Vajk, "Time- and Memory-Efficient Frequent Itemset Discovering Algorithm for Association Rule Mining. " International Journal of Computer Applications in Techology, Special Issue on Data Mining Applications
- Huang, X. (2007). Comparison of interestingness measures for web usage mining: An empirical study. International Journal of Information Technology & Decision Making (IJITDM), 6(1), 15-41.
- Iváncsy, R. , & Vajk, I. (2008). Frequent pattern mining in web log data. Journal of Applied Sciences at Budapest Tech, 3(1), Special Issue on Computational intelligence.
- Jaroszewicz , S. , & Simovici, D. A. (2002). Pruning redundant association rules using maximum entropy principle. Advances in Knowledge Discovery and Data Mining, 6th Pacific-Asia Conference,PAKDD'02.
- H. Han and R. Elmasri, "Learning rules for conceptual structure on the web," J. Intell. Inf. Syst. , Vol. 22, No. 3, pp. 237-256, 2004
- M. Eirinaki and M. Vazirgiannis, "Web mining for web personalization," ACM Trans. Inter. Tech. , Vol. 3, No. 1, pp. 1-27, 2003
- J. Pei, J. Han, B. Mortazavi-Asl, and H. Zhu, "Mining access patterns efficiently from web logs," in PADKK '00: Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications. London, UK: Springer-Verlag, 2000, pp. 396-407
- J. Srivastava, R. Cooley, M. Deshpande, and P. -N. Tan, "Web usage mining: Discovery and applications of usage patterns from web data," SIGKDD Explorations, Vol. 1, No. 2, pp. 12-23, 2000
- J. Punin, M. Krishnamoorthy, and M. Zaki, "Web usage mining: Languages and algorithms," in Studies in Classification, Data Analysis, and Knowledge Organization. Springer-Verlag, 2001
- P. Batista, M. ario, and J. Silva, "Mining web access logs of an on-line newspaper," 2002
- Sudhamathy, G. 2010. Mining web logs: an automated approach. In Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in india (Coimbatore, India, September 16 - 17, 2010). A2CWiC '10. ACM, New York, NY, 1-4. DOI= http://doi. acm. org/10. 1145/1858378. 1858435
- J. Hou and Y. Zhang, "Effectively finding relevant web pages from linkage information. " IEEE Trans. Knowl. Data Eng. , Vol. 15, No. 4, pp. 940-951,2003
Index Terms
Keywords