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Reseach Article

Efficiently Mining Frequent Itemsets using Various Approaches: A Survey

by C. A. Dhote, Sheetal Rathi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 55 - Number 7
Year of Publication: 2012
Authors: C. A. Dhote, Sheetal Rathi
10.5120/8767-2691

C. A. Dhote, Sheetal Rathi . Efficiently Mining Frequent Itemsets using Various Approaches: A Survey. International Journal of Computer Applications. 55, 7 ( October 2012), 28-32. DOI=10.5120/8767-2691

@article{ 10.5120/8767-2691,
author = { C. A. Dhote, Sheetal Rathi },
title = { Efficiently Mining Frequent Itemsets using Various Approaches: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 55 },
number = { 7 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 28-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume55/number7/8767-2691/ },
doi = { 10.5120/8767-2691 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:56:39.191280+05:30
%A C. A. Dhote
%A Sheetal Rathi
%T Efficiently Mining Frequent Itemsets using Various Approaches: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 55
%N 7
%P 28-32
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we present the various elementary traversal approaches for mining association rules. We start with a formal definition of association rule and its basic algorithm. We then discuss the association rule mining algorithms from several perspectives such as breadth first approach, depth first approach and Hybrid approach. Comparison of the various approaches is done in terms of time complexity and I/O overhead on CPU. Finally, this paper prospects the association rule mining and discuss the areas where there is scope for scalability.

References
  1. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo, 1996,Fast discovery of association rules, In Advances in Knowledge Discovery and Data Mining, MIT Press.
  2. R. Agrawal, T. Imielinski, and A. Swami,1993,Mining association rules between sets of items in large databases, In ACM SIGMOD Intl. Conf. Management of Data.
  3. J. Han, J. Pei, and Y. Yin,2000,Mining Frequent Patterns without Candidate Generation, Proc. of the ACM SIGMOD, Dallas, TX.
  4. J. Pei, J. Han, H. Lu, S. Nishio, S. Tang, and D. Yang, 2001,HMine:Hyper-Structure Mining of Frequent Patterns in Large Databases, Proc. of IEEE ICDM, San Jose, California.
  5. R. Agrawal and R. Srikant,1994,Fast Algorithms for Mining Association Rules, Proc. of the 20th Int. Conf. on VLDB, Santiago, Chile.
  6. Y. G. Sucahyo and R. P. Gopalan,2003,CT-ITL: Efficient Frequent Item Set Mining Using a Compressed Prefix Tree with Pattern Growth, Proc. of 14th Australasian Database Conference, Adelaide, Australia.
  7. M. J. Zaki,2000,Scalable Algorithms for Association Mining, IEEE Transactions on Knowledge and Data Engineering, (12, May/June 2000) 372-390.
  8. Agrawal R, Imielinski T, Swami A, 1993,Mining Association Rules between Sets of Items in Large Databases, Proc. of the 1993 ACMSIGMOD Conference. Washington D C, 207-216.
  9. Agrawal R, Srikant R,1994,Fast Algorithm for Mining Association Rules, Proc of the 20th Very Large Data Bases (VLDB 94) Conference. Santiago, Chile.
  10. Park J S, Chen M S, Yu P S, 1995, An effective hash based algorithm for mining association rules, In Proc. Of 1995 ACM SIGMOD. San Jose, 175-186.
  11. Agarwal R, Aggarwal C, Prased V V V,2001,A tree projection algorithm for generation of frequent itemsets, Parallel and distributed Computing, 61(3): 350-371.
  12. Savasere A, Omieeinski E, Navathe S,1995,An efficient algorithm for mining association rules in large databases, Proc. of the 21st International Conference on Very Large Databases. Zurich, Switzerland, 432-443.
  13. Brin S, Motwani R, Ullman J D,1997,Dynamic Itemset counting and implication rules for market basket data, Proc. of the 1997 ACM SIGMOD International Conference on Management of Data.
  14. Han J, Pei J, Yin Y, 2000, Mining frequent patterns without candidate generation, In Proc. of the 2000 ACM SIGM0D Conference on Management of Data. Dallas, TX.
  15. Liu J, Pan Y, Wang K, et al, 2002,Mining frequent item sets by opportunistic projection,Proc Of the Eighth ACM SIGKDD Intl. Conf on Knowledge Discovery and Data Ming. Alberta, Canada, 229-238.
  16. R. Agarwal, C. Aggarwal and V. V. V. Prasad,2000, Depth first generation of long patterns, in Proc. of SIGKDD Conference.
  17. Mohammed Javeed Zaki, Srinivasan Parthasarathy and Wei Li,1997,A Localized Algorithm for Parallel Association Mining, Proc. of 9th Annual ACM Symposium on Parallel Algorithms and Architectures, 321-330.
  18. Agrawal R, Srikant R,1994,Fast Algorithm for Mining Association Rules in Large Databases, San Jose, IBM Alma den Research Center.
  19. Pradeep Shenoy, Jayant Harista, S. Sudarshan, Gaurav Bhalotia, Mayank Bawa, Devavrat Shah,2000,Turbo-charging vertical mining of large databases, Proc. of 2000 ACM SIGMOD International conference on Management of Data, Pg 22-33.
  20. Dao-I Lin, Zvi M. Kedem,2002,Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set, IEEE Transactions on Knowledge and Data Engineering, Vol 14 ,(May 2002),Pg. 552-566.
  21. M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li,1997,New algorithms for fast discovery of association rules, In Proc. 3rd KDD.
  22. R. Shrikant and R. Agrawal ,1996,Mining quantitative association rules in large relational tables, SIGMOD 1996.
Index Terms

Computer Science
Information Sciences

Keywords

Frequent itemset mining breadth first depth first hybrid approach