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A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Hartej Singh, Vinay Dwivedi
10.5120/ijca2016908883

Hartej Singh and Vinay Dwivedi. Article: A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set. International Journal of Computer Applications 137(9):1-4, March 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Hartej Singh and Vinay Dwivedi},
	title = {Article: A Novel Association Rule Algorithm to Discover Maximal Frequent Item Set},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {137},
	number = {9},
	pages = {1-4},
	month = {March},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

Association Rule mining is a sub-discipline of data mining. Apriori algorithm is one of the most popular association rule mining technique. Apriori technique has a disadvantage that before generating a maximal frequent set it generates all possible proper subsets of maximal set. Therefore it is very slow as it requires many database scans before generating a maximal frequent itemset In the method proposed in this paper entire database is scanned only once. Frequency count of all distinct transactions is stored in a hash map. Algorithm maintains an array of tables such that each table in the array contain frequency count of all potential k-itemsets..Binary search and the concept of longest common subsequence are used to efficiently extract maximal frequent itemset. Experimental results show that proposed algorithm performs better than apriori algorithm.

References

  1. R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Database,” Proceedings of the 1993 ACM SIGMOD
  2. International Conference on Management of Data, Vol. 22, Issue 2, 1993, pp. 207-216.
  3. R. Agrawal and R. Srikant, “Fast Algorithms for Mining Association Rules,” Proceedings of the 20th International Conference on Very Large Data Bases, 1994, pp. 487-499.
  4. J. Han, J. Pei, and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 2000, pp. 1-12.
  5. Hidber, C. (1999). Online association rule mining (Vol. 28, No. 2, pp. 145-156). ACM.
  6. J Das, A., Ng, W. K., & Woon, Y. K. (2001, October). “Rapid association rule mining” In Proceedings of the tenth international conference on Information and knowledge management (pp. 474-481). ACM.
  7. J. S. Park, M. S. Chen, and P. S. Yu, "Using a Hash-Based Method with Transaction Trimming for Mining Association Rules," IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No.5, Sep/Oct 1997, pp. 813-825.
  8. NCSA Computational Resources, Retrieved May 14,2006 from http:// www.ncsa.uiuc.edu/UserInfo/
  9. Longest Common Subsequence problem . Available at : wikipedia.org.

Keywords

Association rule mining ,Apriori algorithm, Frequent itemset, Hashing, Longest common subsequence