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Reseach Article

An Efficient Approach of Association Rule Mining on Distributed Database Algorithm

by Neha Saxena, Rakhi Arora, Ranjana Sikarwar, Pradeep Yadav
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 3
Year of Publication: 2013
Authors: Neha Saxena, Rakhi Arora, Ranjana Sikarwar, Pradeep Yadav
10.5120/13991-2011

Neha Saxena, Rakhi Arora, Ranjana Sikarwar, Pradeep Yadav . An Efficient Approach of Association Rule Mining on Distributed Database Algorithm. International Journal of Computer Applications. 81, 3 ( November 2013), 12-16. DOI=10.5120/13991-2011

@article{ 10.5120/13991-2011,
author = { Neha Saxena, Rakhi Arora, Ranjana Sikarwar, Pradeep Yadav },
title = { An Efficient Approach of Association Rule Mining on Distributed Database Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 3 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 12-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number3/13991-2011/ },
doi = { 10.5120/13991-2011 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:06.782863+05:30
%A Neha Saxena
%A Rakhi Arora
%A Ranjana Sikarwar
%A Pradeep Yadav
%T An Efficient Approach of Association Rule Mining on Distributed Database Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 3
%P 12-16
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Applications requiring huge data processing have two main problems, one a massive storage and its supervision and next processing time, when the quantity of data increases. Distributed databases determine the first trouble to a huge amount but second problem increase. Since, current stage is of networking and communication and community are involved in maintenance huge data on networks, therefore, researchers are suggest a range of novel algorithms to raise the throughput of resulted data over distributed databases. Within our research, we are proposing an novel algorithm to process large quantity of data at the a variety of servers and collect the processed data on customer machine as much as necessary.

References
  1. Dr . Sujni Paul, Associate Professor, Department of Computer Science, Karana University, Coimbatore 645 , Tamil Nadu, India
  2. R. Agrawal and R. Srikant , "Fast Algorithms for Mining Association Rules in huge amount of Database, Conf. on very Large Database system .
  3. R. J Agrawal and J. C. Shafer , "Parallel Mining of association Rules in data mining , Distributed Systems held in March 2005
  4. D. W. K Chung ,"Efficient Mining of Asso. Rules in Distributed DB, "IEEE Knowledge & Engg. , vol. 7,
  5. D. W. K Chung,"A Fast Dis Distributed Information computing system, IEEE CS Press, 1997,
  6. Albert Y. N Zomaya, Tark El. J-Ghazawi, Ophir Frieer, 'Distributed Computing for Data Mining'iee conference, held in 1996. International Journal of Computer application and Information Technology, Volume 3, Number 3, April
  7. A. Prodromidis, P. Chan, and S. Stolfo. Chapter Meta learning in Parellal distributed data mining systems: Issues and approaches. AAI/MIT Press, 2001.
  8. Morgan Kaufmann, 1996, pp. 432 Proc. ACM SIGMOD 1-12. 2010- 99 Proc. 20th Int'l 16 IEEE tribute Proc. Parallel and 432-444. national conference of Computer system and application, Volume 4, Number 3, April 2010 .
  9. M. J. Zaki et al. , Parallel Data Mining for Association Rules in partial Memory. ,tech. report TR 618, Computer Science Dept. , Univ. of Rochester, 1997
  10. D. W. Cheung et al. "Efficient Mining of Association Rules in mass Database 'IE Knowledge information Eng. , vol. 8, no. 6, 1996,pp. 916-923;
  11. A. S and R. Wolff , "Communication-Efficient Distributed Mining of Association Rules '. SIGMOD national Conf. on Mgmt. of Data, ACM Press, 2001,pp. 47-48.
  12. T. K Imielinski and A. M Virmani. MSQL: A query language for database management mining. 1999.
  13. H. Kargupta, I. Hamzaoglu, and Brian Stafford. Scalable, distributed data mining-agent architecture. In Heckerman et al.
  14. , page 21.
  15. R. Meo, G. S Psaila, and S. K Ceri. A new SQL like operator for mining association rules. In The VLDB Journal, pages 156–161,
  16. T. Shntani and Kitsuregawa ,'Hash-on Based Parallel Algorithms for Mining Association Rules mining'. Conf. Parallel and Distributed Systems, IEE Press, 1998. 18-34;
  17. Huan, Zhing Lu, Rongsng Xu, WenbJiang,'Apriori-based Algorithm for Association Rules Mining', 9th national Conference on Fuzzy Systems Knowledge Discovery, IEEE Society community, 2007
  18. Rupali Haldukar'Optimization of Association Rule Mining with Genetic Algorithm', International Journal of Computer Science and Engineering (IJCSE), Vol. 2, Issue. 1, May 2011
  19. Huiying Xiawei Le, 'The Research on Improved Association Rules Mining Apriori Algorithm' 2012 sixth International Conference on Fuzzy Systems an & Knowledge Discovery
  20. Mrs. R. Sumithra, Sujni Paul, 'Using distributed apriori association rule and classical apriori mining algorithms for grid based knowledge '2011 third International conference on Computing and Networking Technologies.
Index Terms

Computer Science
Information Sciences

Keywords

Apriori algorithm Association rules parallel and distributed data mining.