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Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining

by Logeswari T, Valarmathi N, Sangeetha A, Masilamani M
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 87 - Number 19
Year of Publication: 2014
Authors: Logeswari T, Valarmathi N, Sangeetha A, Masilamani M
10.5120/15457-3820

Logeswari T, Valarmathi N, Sangeetha A, Masilamani M . Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining. International Journal of Computer Applications. 87, 19 ( February 2014), 4-8. DOI=10.5120/15457-3820

@article{ 10.5120/15457-3820,
author = { Logeswari T, Valarmathi N, Sangeetha A, Masilamani M },
title = { Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining },
journal = { International Journal of Computer Applications },
issue_date = { February 2014 },
volume = { 87 },
number = { 19 },
month = { February },
year = { 2014 },
issn = { 0975-8887 },
pages = { 4-8 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume87/number19/15457-3820/ },
doi = { 10.5120/15457-3820 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:06:19.664119+05:30
%A Logeswari T
%A Valarmathi N
%A Sangeetha A
%A Masilamani M
%T Analysis of Traditional and Enhanced Apriori Algorithms in Association Rule Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 87
%N 19
%P 4-8
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, Enhanced Apriori Algorithm is proposed which takes less scanning time. It is achieved by eliminating the redundant generation of sub-items during pruning the candidate item sets. Both Traditional and Enhanced Apriori algorithms are compared and analysed in this paper.

References
  1. Margret H. Dunham, S. Sridhar, "Data mining Introductory and advanced topics", Pearson Education,Second Edition,2007.
  2. Agrawal, R, Srikant, R, 1994, 'Fast algorithms for mining association rules in large databases', Proc. of 20th Int'l conf. on VLDB: 487-499.
  3. C. Gyorodi, R. Gyorodi. "Mining Association Rules in Large Databases". Proc. of Oradea EMES'02: 45-50, Oradea, Romania, 2002.
  4. M. , Suraj Kumar Sudhanshu, Ayush Kumar and Ghose M. K. , "Optimized association rule mining using genetic algorithm Anandhavalli Advances in Information Mining" , ISSN: 0975–3265, Volume 1, Issue 2, 2009, pp-01-04
  5. Han, J, Pei, J, Yin, Y 2000, 'Mining Frequent Patterns without Candidate Generation', Proc. of ACM-SIGMOD.
  6. Daniel Hunyadi, "Performance comparison of Apriori and FP-Growth algorithms in generating association rules", Proceedings of the European Computing Conference.
  7. Goswami D. N. et. al. "An Algorithm for Frequent Pattern Mining Based On Apriori", (IJCSE) International Journal on Computerm Science and Engineering Vol. 02, No. 04, 2010, 942-947
  8. Ms Shweta and Dr. Kanwal Garg "Mining Efficient Association Rules Through Apriori Algorithm Using Attributes and Comparative Analysis of Various Association Rule Algorithms", International Journal of Advanced Research in Computer Science and Software Engineering Volume 3, Issue 6, June2013.
  9. Pulari. s. s et al, "Understanding Rule Behavior through Apriori Algorithm over Social Network Data", Global Journal of Computer Science and Technology Volume 12 Issue 10 Version 1. 0 May 2012.
  10. "Fast Algorithms for Mining Association Rules", IBM Almaden Research Center, 650 Harry Road, San Jose, CA 95120.
Index Terms

Computer Science
Information Sciences

Keywords

Candidate generation frequent itemsets transaction_size support count threshold.

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