Improved Apriori Algorithm with Pruning Unnecessary Candidate Set for Reducing Execution Time

Authors(2) :-Prof. Neha Khare, Priya Shrivastava

Data mining which is also known as Knowledge Discovery in the databases (KDD) is an important research area in today’s time. One of the important techniques in data mining is frequent pattern discovery. Finding co-occurrence relationships between items is the focus of this technique. The active research topic for KDD is association rule mining and many algorithms have been developed on this. This algorithm is used for finding associations in the item-sets. Its application areas include medicine, World Wide Web, telecommunication and many more. Efficiency has been an issue of concern for many years in mining association rules. Till date the researchers of data mining have worked a lot on improving the quality of association rule mining and have succeeded to a great extent. There are many algorithms for mining association rules. Apriori algorithm is the mostly used algorithm which is used to determine the item-sets, which are frequent, from a large database. It extracts the association rules which in turn are used for knowledge discovery. Apriori is based on the approach of finding useful patterns from various datasets. There are lot many other algorithms that are used from association rule mining and are based on Apriori algorithm. Although it is a traditional approach, it still has many shortcomings. It suffers from the deficiency of unnecessary scans of the database while looking for frequent item-sets as there is frequent generation of candidate item-sets that are not required. Also there are sub item-sets generated which are redundant and algorithm involves repetitive searching in the database. This work has been done to reduce the redundant generation of sets. The large dataset is scanned only once. As a result, the overall time of execution is reduced. Also the number of transactions to be scanned are reduced.

Authors and Affiliations

Prof. Neha Khare
Takshshila Institute of Engineering and Technology, Jabalpur, Madhya Pradesh, India
Priya Shrivastava
Takshshila Institute of Engineering and Technology, Jabalpur, Madhya Pradesh, India

Apriori, Datamining, Frequent Item, Association Rules, Transactions

  1. R. Agrawal, R. Srikant, "Fast Algorithms for Mining Association Rules", pp. 487- 499.
  2. X. Liu, P. He, "The Research of Improved Association Rules Mining Apriori ALgorithm", Proceedings of the Third International Conference on Machine Learning and Cybermetics, Shanghai, 26-29 August 2015, pp. 1577-1579.
  3. J. Lei, B. Zhang, J. Li, "A new improvement on Apriori Algorithm", International Conference on Computational Intelligence and Security, Vol. 1, IEEE, 2015, pp. 840-844.
  4. Y. Xie, Y. Li, C. Wang, M. Lu, "The Optimization and Improvement of the Apriori Algorithm", Education Technology and Training, International Workshop on Geoscience and Remote Sensing, ETT and GRS, Vol. 2, IEEE, 2015, pp. 663- 665.
  5. Z. Changsheng, L. Zhongyue, Z. Dongsong, "An Improved Algorithm for Apriori", First International Workshop on Education Technology and Computer Science, 2015, pp. 995-998.
  6. L. Jing et. al, "An Improved Apriori Algorithm for Early Warning of Equipment Failure", 2015, pp. 450-452.
  7. K. Shah, S. Mahajan, "Maximizing the Efficiency of Parallel Apriori Algorithm", International Conference on Advances in Recent Technologies in Communication and Computing, 2015, pp. 107-109.
  8. H. Wu, Z. Lu, L. Pan, R. Xu, W. Jiang, "An Improved Apriori-based Algorithm for Association Rules Mining", Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2014, pp. 51-55.
  9. Y. Liu, "Study on Application of Apriori Algorithm in Data Mining", Second International Conference on Computer Modelling and Simulation, 2013, pp. 111- 114.
  10. L. Wu, K. Gong, Y. He, X. Ge, J. Cui, "A Study of Improving Apriori Algorithm", 2013, pp. 1-4.
  11. P. Sandhu, D. Dhaliwal, S. Panda, A. Bisht, "An Improvement in Apriori algorithm Using Profit And Quantity", Second International Conference on Computer and Network Technology, 2012, pp. 3-7.
  12. L. Lu, P. Lu, "Study On An Improved Apriori Algorithm And Its Application In Supermarket", the research on Uncertain Reasoning Mechanism of Fuzzy Concept Map, pp. 441-443.
  13. G. Wang, X. Yu, D. Peng, Y. Cui, Q. Li, "Research of Data Mining Based on Apriori algorithm in Cutting Database", 2012, pp. 3765-3768.
  14. V. Sharma, M. Beg, "A Probabilistic Approach to Apriori Algorithm", International Conference on Granular Computing, IEEE, 2013, pp. 225-243.
  15. Y. Shi, Y. Zhou, "An Improved Apriori Algorithm", International Conference  on Granular Computing, IEEE, 2013 pp. 759-762.
  16. Y. Shaoqian, "A kind of improved algorithm for weighted Apriori and application to Data Mining", The 5th International Conference on Computer Science & Education Hefei, China, August 24–27, 2013, pp. 507-510.
  17. D. Ping, G. Yongping, "A New Improvement of Apriori Algorithm for Mining Association Rules", International Conference on Computer Application and System Modelling (ICCASM), 2013, pp. V2-529.
  18. Y. Zhou, W. Wan, J. Liu, L. Cai, "Mining Association Rules Based on an Improved Apriori Algorithm", 2012, pp. 414-418.

Publication Details

Published in : Volume 3 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 603- 606
Manuscript Number : IJSRSET173330
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Prof. Neha Khare, Priya Shrivastava, " Improved Apriori Algorithm with Pruning Unnecessary Candidate Set for Reducing Execution Time, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 3, pp.603- 606, May-June-2017.
Journal URL :

Follow Us

Contact Us