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

Efficient Approach for Large Database Compressed In Association Mining

by Sunichchha Chauhan, Vimal Tiwari
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 28
Year of Publication: 2018
Authors: Sunichchha Chauhan, Vimal Tiwari
10.5120/ijca2018916632

Sunichchha Chauhan, Vimal Tiwari . Efficient Approach for Large Database Compressed In Association Mining. International Journal of Computer Applications. 179, 28 ( Mar 2018), 30-33. DOI=10.5120/ijca2018916632

@article{ 10.5120/ijca2018916632,
author = { Sunichchha Chauhan, Vimal Tiwari },
title = { Efficient Approach for Large Database Compressed In Association Mining },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2018 },
volume = { 179 },
number = { 28 },
month = { Mar },
year = { 2018 },
issn = { 0975-8887 },
pages = { 30-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number28/29138-2018916632/ },
doi = { 10.5120/ijca2018916632 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:56:46.635947+05:30
%A Sunichchha Chauhan
%A Vimal Tiwari
%T Efficient Approach for Large Database Compressed In Association Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 28
%P 30-33
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Large Amount of data is being using very rapidly in the world. It to be compressed takes much more time and takes lot of effort to process these data for knowledge discovery and decision making. Data compression technique is one of good solutions to be reduce size of data that can be save more time the time of discovering useful knowledge by using appropriate methods, for example Data mining. Data mining is used to help users discover interesting and useful knowledge more easily to decision making purpose. It is more and more popular to apply the association rule mining in recent years because of its wide applications in many fields such as stock analysis, web log mining, medical diagnosis, customer market analysis and bioinformatics. In this paper the main focus in on association rule mining and data pre-process with data compression. In this paper we analysis the methods simple Apriori, Partion based Apriori and Apriori with compressed dataset. We compare these three methods on the basis of minimum support, minimum confidence, number of records and execution times.

References
  1. M. C. Hung, S. Q. Weng, J. Wu, and D. L. Yang, "Efficient Mining of Association Rules Using Merged Transactions," in WSEAS Transactions on Computers, Issue 5, Vol.5, pp. 916-923, 2006.
  2. M. Z. Ashrafi, D. Taniar, and K. Smith, "A Compress-Based Association Mining Algorithm for Large Dataset," in Proceedings of International Conference on Computational Science, pp. 978-987, 2003.
  3. R. Agrawal and R. Srikant, "Fast Algorithms for Mining Association Rules," in Proceedings of the 20th International Conference on Very Large Data Bases, pp. 487-499, 1994.
  4. D. Xin, J. Han, X. Yan, and H. Cheng, "Mining Compressed Frequent-Pattern Sets," in Proceedings of the 31st international conference on Very Large Data Bases, pp. 709-720, 2005.
  5. G. Grahne and J. Zhu, "Fast algorithms for frequent itemset mining using FP-trees," IEEE Transactions on Knowledge and Data Engineering, Vol. 17, pp. 1347-1362, 2005.
  6. M. Z. Ashrafi, D. Taniar, and K. Smith, "A Compress-Based Association Mining Algorithm for Large Dataset," in Proceedings of International Conference on Computational Science, pp. 978-987, 2003.
  7. Ashrafi and K. Smith, “Data Compression-Based Mining Algorithm for Large Dataset," in Proceedings of International Conference on Computational Science, 2003.
  8. D. I. Lin and Z. M. Kedem, "Pincer-search: an efficient algorithm for discovering the maximum frequent set," IEEE Transactions on Knowledge and Data Engineering, Vol. 14, pp. 553-566, 2002.
  9. E. Hullermeier, "Possibilistic Induction in Decision-Tree Learning," in Proceedings of the 13th European Conference on Machine Learning, pp. 173-184, 2002.
  10. Cheung, W., "Frequent Pattern Mining without Candidate generation or Support Constraint." Master’s Thesis, University of Alberta, 2002.
  11. Huang, H., Wu, X., and Relue, R. Association Analysis with One Scan of Databases. Proceedings of the 2002 IEEE International Conference on Data Mining. 2002.
  12. Zaki, M. J., Parthsarathy, S., Ogihara, M., and Li, W. New Algorithms for Fast Discovery of Association Rules. KDD, 283-286. 1997. Agarwal, R., Aggarwal, C., and Prasad, V.V.V. 2001.
  13. Goulbourne, G., Coenen, F., and Leng, P. H. Computing association rule using partial totals. In Proceedings of the 5th European Conference on Principles and Practice of Knowledge Discovery in Databases, 54-66. 2001.
  14. Pei, J., Han, J., Nishio, S., Tang, S., and Yang, D. H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases. Proc.2001 Int.Conf.on Data Mining. 2001.
  15. Orlando, S., Palmerini, P., and Perego, R. Enhancing the Apriori Algorithm for Frequent Set Counting. Proceedings of 3rd International Conference on Data Warehousing and Knowledge Discovery. 2001.
  16. Grahne, G., Lakshmanan, L., and Wang, X. 2000. Efficient mining of constrained correlated sets. In Proc. 2000. Int. Conf. Data Engineering (ICDE’00), San Diego, CA, pp. 512–521.
Index Terms

Computer Science
Information Sciences

Keywords

Association rule Apriori Algorithem merged transaction quantification table.

Powered by PhDFocusTM