CFP last date
20 January 2025
Reseach Article

Web Log Mining using Improved Version of Apriori Algorithm

by Suneetha K R, Krishnamoorti R
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 6
Year of Publication: 2011
Authors: Suneetha K R, Krishnamoorti R
10.5120/3569-4923

Suneetha K R, Krishnamoorti R . Web Log Mining using Improved Version of Apriori Algorithm. International Journal of Computer Applications. 29, 6 ( September 2011), 23-27. DOI=10.5120/3569-4923

@article{ 10.5120/3569-4923,
author = { Suneetha K R, Krishnamoorti R },
title = { Web Log Mining using Improved Version of Apriori Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 6 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 23-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number6/3569-4923/ },
doi = { 10.5120/3569-4923 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:05.007873+05:30
%A Suneetha K R
%A Krishnamoorti R
%T Web Log Mining using Improved Version of Apriori Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 6
%P 23-27
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association Rule mining is one of the important and most popular data mining technique. It extracts interesting correlations, frequent patterns and associations among sets of items in the transaction databases or other data repositories. Most of the existing algorithms require multiple passes over the database for discovering frequent patterns resulting in a large number of disk reads and placing a huge burden on the input/output subsystem. In order to reduce repetitive disk read, a novel method of top down approach is proposed in this paper. The improved version of Apriori Algorithm greatly reduces the data base scans and avoids generation of unnecessary patterns which reduces data base scan, time and space consumption.

References
  1. Agrawal, R., Imielinski, T., and Swami, A. N. Mining Association Rules Between Sets of Items in Large Databases. Proceedings of the ACM SIGMOD, International Conference on Management of Data, pp.207-216, 1993.
  2. Agrawal. R., and Srikant. R., Fast Algorithms for Mining Association Rules, Proceedings of 20th International Conference of Very Large Data Bases. pp.487-499,1994.
  3. Agrawal. R., and Srikant. R. Mining Sequential Patterns. Proceedings of 11th International Conference on Data Engineering, IEEE Computer Society Press, pp.3-14, 1995.
  4. Eui-Hong Han, George Karypis, and Kumar, V. Scalable Parallel Data Mining for Association Rules. IEEE Transaction on Knowledge and Data Engineering, 12(3), pp.728-737, 2000.
  5. Han, J., Dong, G., and Yin, Y. Efficient Mining of Partial Periodic Patterns in Time Series Database. Proceedings of 15th IEEE International Conference on Data Engineering, pp.106–115, 1999.
  6. Han, J., Jian, Pei., and Yiwen, Yin. Mining Frequent Patterns without Candidate Generation. Proceedings of ACM International conference on Management of Data, 29( 2), pp.1-12, 2000.
  7. Han, J., Jian, Pei., Yiwen, Yin, and Runying, Mao. Mining Frequent Pattern without Candidate Generation: A Frequent-Pattern Tree Approach. Journal of Data Mining and Knowledge Discovery, 8, pp.53-87, 2004.
  8. Jong Park, S., Ming-Syan, Chen, and Yu, P. S. Using a Hash-Based Method with transaction Trimming for Mining Association Rules. IEEE Transactions on Knowledge and Data Engineering, 9(5), pp.813-825,1997.
  9. Juan, M. A., Gustavo, H., and Rossi. An Approach to Discovering Temporal Association Rules. Proceedings of the ACM Symposium on Applied Computing, 1, pp.234-239,2000.
  10. Keith, Chan., and Wai-Ho, A. An Effective Algorithm for Mining Interesting Quantitative Association Rules. Proceedings of the ACM Symposium on Applied Computing, pp. 88.-90,1997.
  11. Lakshmanan, V., S., Carson Kai-Sang, L., and T. Raymond. The Segment Support Map: Scalable Mining of Frequent Itemsets. Journal of ACM SIGKDD Explorations Newsletter, 2( 2), pp.21-27, 2000.
  12. Mata, J., Alvarez, J. L., and Riquelme, J. C. Evolutionary Computing and Optimization: An Evolutionary Algorithm to Discover Numeric Association Rules. Proceedings of ACM Symposium on applied Computing, pp. 590-594, 2002.
  13. Srivastava, J., Cooley, R., Deshpande, M., and Tan, P. N. Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data. Journal of ACM Special Interest Group on Knowledge Discovery and Data Mining Explorations, 1(2), pp.12-23, 2000.
  14. Velu, C. M., Ramakrishnan, M., Somu, V., and Logznathan, V. Efficient Association Rules for Data Mining. International Journal of Soft Computing , 2, pp.21-36, 2007.
  15. Wang Tong, and He Pi-Lian. Web Log Mining by Improved Apriori All Algorithm. Transaction on Engineering Computing and Technology, 4, pp.97-100, 2005.
  16. Wei Zhang, Zhang Wei, Dongme Sun Shaohua Teng and Haibin Zhu. An Algorithm to Improve Effectiveness of Apriori. Proceedings of 6th IEEE International Conference on Cognitive Informatics, pp.385-390, 2007.
  17. Xiang-Wei Liu, and Pi-Lian He. The Research of Improved Association Rules Mining Apriori Algorithm. Proceedings of 3rd International Conference on Machine Learning and Cybernetics, pp.1577-1579, 2004.
  18. Yiwu Xie, Yutong Li, Chunli Wang, and Mingyu Lu. The Optimization and Improvement of the Apriori Algorithm. Proceedings of IEEE International Symposium on Intelligent Information Technology Application Workshops, pp. 1101-1103, 2008.
  19. Zhao Hong, Gang yang, Lei Wang, and Ying liu. An Implementation of Improved Apriori Algorithm. Proceedings of 8th IEEE International Conference on Machine Learning and Cybernetics, pp.1565-1569, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Association rule Apriori algorithm Frequent pattern