CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Performance based Transaction Reduction Algorithm for Discovering Frequent Patterns

by V. Vijayalakshmi, A. Pethalakshmi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 98 - Number 4
Year of Publication: 2014
Authors: V. Vijayalakshmi, A. Pethalakshmi
10.5120/17170-7242

V. Vijayalakshmi, A. Pethalakshmi . A Performance based Transaction Reduction Algorithm for Discovering Frequent Patterns. International Journal of Computer Applications. 98, 4 ( July 2014), 18-21. DOI=10.5120/17170-7242

@article{ 10.5120/17170-7242,
author = { V. Vijayalakshmi, A. Pethalakshmi },
title = { A Performance based Transaction Reduction Algorithm for Discovering Frequent Patterns },
journal = { International Journal of Computer Applications },
issue_date = { July 2014 },
volume = { 98 },
number = { 4 },
month = { July },
year = { 2014 },
issn = { 0975-8887 },
pages = { 18-21 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume98/number4/17170-7242/ },
doi = { 10.5120/17170-7242 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:25:51.194344+05:30
%A V. Vijayalakshmi
%A A. Pethalakshmi
%T A Performance based Transaction Reduction Algorithm for Discovering Frequent Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 98
%N 4
%P 18-21
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association rules are the main technique to determine the frequent item set in data mining. When a large number of item sets are processed by the database, it needs to be scanned multiple times. Consecutively, multiple scanning of the database increases the number of rules generation, which then consume more system resources. Existing approach TR-BAM scans the unnecessary transaction which takes more time to find frequent item set. This paper presents a modified transaction reduction technique named PBTRA which reduces the scanning times by cutting down the unnecessary transaction row. So, the corresponding item set is extracted directly without moving for entire database. Moreover, it exploits horizontal transaction of the matrix that automatically reduces the entire database scanning. Experimental results validate the performance of the proposed approach and expose that proposed method is more effective and efficient than previously proposed algorithm.

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,
  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. M. S. Chen, J. Han, and P. S. Yu. Data mining: An overview from a database Perspective. IEEE Trans. Knowledge and Data Engineering, 8:866-883, 1996.
  4. U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthurusamy. Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press, 1996.
  5. Agarwal, R. Agarwal, C. and Prasad V. , A tree projection algorithm for generation of frequent item sets. In J. Parallel and Distributed Computing, 2000
  6. L. Cheng and B. B. Wang, "An Improved Apriori Algorithm for Mining Association Rules, " Comput. Eng. , Shanghai, vol. 28(7), pp. 104-105, 2002.
  7. Sheng Chai; Jia Yang; Yang Cheng;," The Research of Improved Apriori Algorithm for Mining Association Rules," Service System and Service Management, 2007 International Conference on, vol. , no. ,pp. 1-4, 9-11 June 2007
  8. Li Xiaohong,Shang Jin. An improvement of the new Apriori algorithm [J]. Computer science, 2007,34 (4) :196-198. 2007
  9. Wanjun Yu, Xiachun Wang and et. al, (2008), "The Research of Improved Apriori Algorithm for Mining Association Rules", pp. 513-516
  10. PEI Guying. A Fast Algorithm for Mining of Association Rules Based on Boolean Matrix. Automation & Instrumentation. 2009; 5: 16-18.
  11. LV Taoxia, LIU Peiyu. Algorithm for Generating Strong Association Rules Based on Matrix. Application Research of Computers. 2011; 28(4): 1301- 1303
  12. ZHANG Zhongping, LI Yan, YANG Jing. Frequent Itemsets Mining Algorithm Based on Matrix. Computer Engineering. 2009; 35(1): 84-85.
  13. S. Prakash, R. M. S. Parvathi. , An Enhanced Scaling Apriori for Association Rule Mining Efficiency. European Journal of Scientific Research, ISSN 1450-216X Vol. 39 No. 2 (2010), pp. 257-264
  14. Wang Lifeng. An Efficient Association Rule Algorithm Based on Boolean Matrix. International Review on Computers and Software. 2012; 7(2): 695-700.
  15. Database URL: http://www2. cs. Uregina . ca/~ dbd/ cs831/ notes/ itemsets/ datasets. php.
  16. V. Vijayalakshmi, A. Pethalakshmi. , "Mining of Frequent Itemsets With an Enhanced Apriori Algorithm", IJCA, Vol 81-No. 4, November 2013
Index Terms

Computer Science
Information Sciences

Keywords

Frequent Item Set Apriori Support Count. TR-BAM PBTRA

Powered by PhDFocusTM