We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Mining of Frequent Itemsets with an Enhanced Apriori Algorithm

by V. Vijayalakshmi, A. Pethalakshmi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 4
Year of Publication: 2013
Authors: V. Vijayalakshmi, A. Pethalakshmi
10.5120/13997-2033

V. Vijayalakshmi, A. Pethalakshmi . Mining of Frequent Itemsets with an Enhanced Apriori Algorithm. International Journal of Computer Applications. 81, 4 ( November 2013), 1-4. DOI=10.5120/13997-2033

@article{ 10.5120/13997-2033,
author = { V. Vijayalakshmi, A. Pethalakshmi },
title = { Mining of Frequent Itemsets with an Enhanced Apriori Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 4 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-4 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number4/13997-2033/ },
doi = { 10.5120/13997-2033 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:10.789947+05:30
%A V. Vijayalakshmi
%A A. Pethalakshmi
%T Mining of Frequent Itemsets with an Enhanced Apriori Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 4
%P 1-4
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Apriori algorithm is a classical algorithm of association rule mining and widely used for mining association rule which uses frequent item. This classical algorithm is inefficient due to so many scans of database. And if the database is large, it takes too much time to scan the database. To reduce these two limitations, this paper proposes a new technique called TR-BAM for mining frequent patterns in large databases by implementing a Bit Array Matrix. The whole database is scanned only once and the data is compressed in the form of a Bit Array Matrix. The frequent patterns are then mined directly from this Matrix. Appropriate operations are designed and performed on matrices to achieve efficiency.

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. Peng Gong, Chi Yang, and Hui Li, "The Application of Improved Association Rules Data Mining Algorithm Apriori in CRM", Proceedings of 2nd International Conference on Pervasive Computing and Applications, 2007
  9. Li Xiaohong,Shang Jin. An improvement of the new Apriori algorithm [J]. Computer science, 2007,34 (4) :196-198. 2007
  10. Wanjun Yu, Xiachun Wang and et. al, (2008), "The Research of Improved Apriori Algorithm for Mining Association Rules", pp. 513-516
  11. PEI Guying. A Fast Algorithm for Mining of Association Rules Based on Boolean Matrix. Automation & Instrumentation. 2009; 5: 16-18.
  12. LV Taoxia, LIU Peiyu. Algorithm for Generating Strong Association Rules Based on Matrix. Application Research of Computers. 2011; 28(4): 1301- 1303
  13. ZHANG Zhongping, LI Yan, YANG Jing. Frequent Itemsets Mining Algorithm Based on Matrix. Computer Engineering. 2009; 35(1): 84-85.
  14. 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
  15. Wang Lifeng. An Efficient Association Rule Algorithm Based on Boolean Matrix. International Review on Computers and Software. 2012; 7(2): 695-700.
Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Frequent Item Set Apriori Bit Array Matrix

Powered by PhDFocusTM