Survey on Association Rule Mining and Its Approaches

Authors

  • M Shridhar Department of Computer Science and Engineering, Madhav Institute of Technology and Science, Gwalior
  • M Parmar Department of Computer Science and Engineering, Madhav Institute of Technology and Science, Gwalior

Keywords:

component Apriori algorithm, frequent pattern, association rule mining, Support, minimum support threshold, multiple scan, FP Growth algorithm, regression technique

Abstract

Apriori calculation has been basic calculation in association rule mining. Principle proposition of this calculation is to discover valuable examples between various arrangements of information. It is the least complex calculation yet having numerous downsides. Numerous specialists have been accomplished for the improvement of this calculation. This paper does a study on couple of good improved methodologies of Apriori calculation. This will be truly exceptionally supportive for the up and coming specialists to locate some new thoughts of this methodology.

References

. S. Paul, “An Optimized Distributed Association Rule Mining Algorithm In Parallel And Distributed Data Mining With XML Data For Improved Response Time”, International Journal of Computer Science and Information Technology, Volume 2, Number 2, April 2010.

. M.N. Moreno, S. Segrera and V.F. López, “Association Rules: Problems”, Solutions and new application Universidad de Salamanca, Plaza Merced S/N, 37008, Salamanca.

. K.P. Kumar and S. Arumugaperumal, “Association Rule Mining and Medical Application; A Detailed Survey”, International Journal of Computer Application(0975-8887), Volume 80, number 17, October 2013.

. E. Bala Krishna, B. Rama, A. Nagaraju, "A Survey on Effective Mining of Negative Association Rules from Huge Databases", International Journal of Computer Sciences and Engineering, Vol.3, Issue.9, pp-220-223, 2015.

. V. Kavi, D. Joshi , "A Survey on Enhancing Data Processing of Positive and Negative Association Rule Mining", International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.139-143, 2014.

. C. Wang, R. Li, and M. Fan, “Mining Positively Correlated FrequentItemsets,” Computer Applications, vol. 27, pp. 108-109, 2007

. J. Pei, J. Han, and H. Lu, “Hmine: Hyper-structure mining of frequent patterns in large databases”, In ICDM, 2001, pp441–448.

. N. Sethi, P. Sharma, "Mining Frequent Pattern from Large Dynamic Database Using Compacting Data Sets", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.3, pp.31-34, 2013.

. R. Trikha, J. Singh, “Improving the efficiency of apriori algorithm by adding new parameters”, International Journal for Multi-Disciplinary Engineering and Business Management, Volume-2, Issue-2, June-2014

. M. Al-Maolegi, B. Arkok, “An improved apriori algorithm for association rules”, International Journal on Natural Language Computing (IJNLC) Vol. 3, No.1, February 2014

Downloads

Published

2025-11-11

How to Cite

[1]
M. Shridhar and M. Parmar, “Survey on Association Rule Mining and Its Approaches”, Int. J. Comp. Sci. Eng., vol. 5, no. 3, pp. 129–135, Nov. 2025.

Issue

Vol. 5 No. 3 (2017): IJCSE March Edition

Section

Survey Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.