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PFIMII: Parallel Frequent Itemset Mining using Interval Intersection

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Neelam Duhan, Parul Tomar, Amit Siwach

Neelam Duhan, Parul Tomar and Amit Siwach. PFIMII: Parallel Frequent Itemset Mining using Interval Intersection. International Journal of Computer Applications 156(13):10-15, December 2016. BibTeX

	author = {Neelam Duhan and Parul Tomar and Amit Siwach},
	title = {PFIMII: Parallel Frequent Itemset Mining using Interval Intersection},
	journal = {International Journal of Computer Applications},
	issue_date = {December 2016},
	volume = {156},
	number = {13},
	month = {Dec},
	year = {2016},
	issn = {0975-8887},
	pages = {10-15},
	numpages = {6},
	url = {},
	doi = {10.5120/ijca2016912586},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Data Mining techniques are helpful to uncover the hidden predictive patterns from large masses of data. Frequent item set mining also called Market Basket Analysis is one the most famous and widely used data mining technique for finding most recurrent itemsets in large sized transactional databases. Many methods are devised by researchers in this field to carry out this task, some of these are Apriori, Partitioning approach and Interval Intersection etc. In this paper, a new approach is being proposed to find the frequent item sets using Interval Intersection and Apriori Algorithm, which produces results in parallel on several partitions of dataset. For representing the item sets, interval sets are used and for calculating the support count, interval intersection operation is used. The experimental results indicate that the proposed approach is accurate and produces results faster than Apriori Algorithm.


  1. Aggaraval R; Imielinski.t; Swami.A. “Mining Association Rules between Sets of Items in Large Databases”. ACM SIGMOD Conference. Washington DC, USA, 2013.
  2. Jiawei Han And Micheline kamber, “Frequent item set mining methods”, Data Mining concepts and techniques.
  3. Moore,R. E, R. Baker Kearfott and M. J. Cloud, “Introduction to interval analysis”, Siam,2009
  4. Siddharth Shah, N. C. chauhan, S. D. Bhanderi, “Incremental Mining of association rule: a survey”, International journal of computer science and information technology, vol. 3(3), 2012, 4071-4074
  5. H. Li, Yi Wang, D. Zhang. PFP: Parallel FP-Growth for Recommendation, Proceedings of the 2008 ACM conference on Recommender systems, October. 2008,pp. 23-2.
  6. Yungho-Leu, Vania Utami, “A new frequent item set mining algorithm based on interval intersection” in proceedings of Conference on machine learning and cybernatics, guangzhou 12-15 April, 2015.
  7. R. Bhaskar, S. Laxman, A. Smith, and A. Thakurta, “Discovering frequent patterns in sensitive data,” in Proc. 16th ACM SIGKDD Int. Conf. Knowl. DiscoveryData Mining, 2010, pp. 503–512.
  8. Ashok Savasere, Edward Omiecinski, Shamkant Navathe, “An efficient algorithm for mining association rules in large databases”, College of computing, Georgia Institute of Technology 2010.
  9. D. Cheung, J. Han, V. Ng, and C. Y. Wong. Large Databases: An Incremental Updating Technique. Proceedings of the 12th International Conference on Data Engineering, pages 106—114, February 1996.


Frequent Item set mining, A-priori, Partition Algorithm, Interval Intersection, Support count.