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Reseach Article

F3 Algorithm for Association Rules

by Rina Raval
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 164 - Number 10
Year of Publication: 2017
Authors: Rina Raval
10.5120/ijca2017913690

Rina Raval . F3 Algorithm for Association Rules. International Journal of Computer Applications. 164, 10 ( Apr 2017), 6-11. DOI=10.5120/ijca2017913690

@article{ 10.5120/ijca2017913690,
author = { Rina Raval },
title = { F3 Algorithm for Association Rules },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2017 },
volume = { 164 },
number = { 10 },
month = { Apr },
year = { 2017 },
issn = { 0975-8887 },
pages = { 6-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume164/number10/27517-2017913690/ },
doi = { 10.5120/ijca2017913690 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:10:56.262389+05:30
%A Rina Raval
%T F3 Algorithm for Association Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 164
%N 10
%P 6-11
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Frequent pattern mining or association rule mining has been very fascinating research topic. It gives association rules which are nothing else but relationships amongst data. These relationships play vital role to make decision in market based analysis, medical applications, banking and many other organizations. Amongst several algorithms provided for frequent pattern mining, time necessitated is always very important aspect to be considered. The breakthrough approach named F3 algorithm, finds frequent patterns by considering quantity of individual item in single transaction rather than item’s presence. Afterwards it finds supplementary appealing patterns from profit of items. This approach not only reduces the time for finding frequent patterns, but also endow with new effective pat-terns which act as a key to improve business utility.

References
  1. Karl Aberer,(2007-2008), Data mining-A short in-troduction[Online],Available:http://lsirwww.epfl.ch/courses/dis /2003ws/lecturenotes/week13Dataminng print.pdf
  2. Agrawal, R. and Srikant, R. 1995.” Mining sequential patterns”, P. S. Yu and A. S. P. Chen, Eds. In:IEEE Computer Society Press, Taipei, Taiwan, 3{14}.
  3. R.Divya, S.Vinod kumar ,”Survey on AIS,Apriori and FP-Tree algorithms”,In: International Journal of Computer Science and Management Research Vol 1 Issue 2 September 2012, ISSN 2278-733X
  4. Goswami D.N., Chaturvedi Anshu.,Raghuvanshi C.S.,” An Algorithm for Frequent Pattern Mining Based On Apriori”, In: Goswami D.N . et. al./ (IJCSE) International Journal on Computer Science and Engineering ,,Vol. 02, No. 04, 2010, 942 -947, ISSN : 0975-3397
  5. Sheila A. Abaya, “Association Rule Mining based on Apriori Algorithm in Minimizing Candidate Generation”,In:International Journal of Scientific & Engineering Research Volume 3, Issue 7, July-2012
  6. Zhang Changsheng, Li Zhongyue, Zheng Dongsong,” An Improved Algorithm for Apriori”,In: IEEE,First International Workshop on Education Technology and Computer Science,2009
  7. Ms. Sanober Shaikh, Ms. Madhuri Rao,Dr. S. S. Mantha,” A New Association Rule Mining Based On Frequent Item Set”,In : CS & IT-CSCP 2011
  8. Mamta Dhanda,” An Approach To Extract Efficient Frequent Patterns From Transactional Database”,In: International Journal of Engineering Science and Technology (IJEST), Vol.3 No.7 July 2011, ISSN:0975-5462
  9. Andrew Kusiak, Association Rules -The Apriori algorithm[Online],Available:http://www.engineering.uio wa.edu/~comp/Public/ Apriori.pdf
  10. Mamta Dhanda, Sonali Guglani , Gaurav Gupta, ”Mining Efficient Association Rules Through Apriori Algorithm Using Attributes ”, In: International Jour-nal of Computer Science and Technology Vol 2,Issue 3,September 2011,ISSN:0976-8491
  11. Hilderman R. J., Hamilton H. J.,"Knowledge Discovery and Interest Measures ",In: Kluwer Aca-demic Publishers, Boston, 2002
  12. Ku mar, Association analysis: basic concepts and algorithms [Online], Available: http:// www-users.cs.umn.edu ~kumar/dmbook/ch6.pdf
  13. Tan, Steinbach, Ku mar, (2004, April18 ), Introduction to Data mining[On line],Available:http://wwwusers.cs.umn.edu/~kumar/dmbook/dmsli des/chap6_basic_association_analysis.pdf
  14. R. Agrawal, T. Imielinski, and A. Swami, “Mining Association Rules between Sets of Items in Large Databases”, In: Proceedings of the 1993 Inter-national Conference on Management of Data (SIGMOD 93), pages 207-216, May 1993
  15. Lu,S.,Hu , H., Li, F., “Mining Weighted Association Rules”, In: Intelligent Data Analysis, vol.5 , no. 3, pp.211 - 225, August 2001
  16. Parvinder S. Sandhu, Dalvinder S. Dhaliwal.S.N. Panda,Atul Bisht,"An Improvement in Apriori Algo-rithm Using Profit and Quantity" , In: 2010 Second International Conference on Computer and Network Technology, April 23-April 25,ISBN: 978-0-7695-4042-9,Bangko k, Thailand
  17. Jianying Hu, Aleksandra Mojsilovic, "High - utility pattern mining: A method for discovery of high-utility item sets",In: Pattern Recognition vol. 40, no. 11, pp.3317-3324, November 2007
Index Terms

Computer Science
Information Sciences

Keywords

PW-factor Q-factor F3 Algorithm