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Parallel Association Rule Mining on Heterogeneous System

by Rakhi Garg, P. K. Mishra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 14
Year of Publication: 2010
Authors: Rakhi Garg, P. K. Mishra
10.5120/295-459

Rakhi Garg, P. K. Mishra . Parallel Association Rule Mining on Heterogeneous System. International Journal of Computer Applications. 1, 14 ( February 2010), 81-85. DOI=10.5120/295-459

@article{ 10.5120/295-459,
author = { Rakhi Garg, P. K. Mishra },
title = { Parallel Association Rule Mining on Heterogeneous System },
journal = { International Journal of Computer Applications },
issue_date = { February 2010 },
volume = { 1 },
number = { 14 },
month = { February },
year = { 2010 },
issn = { 0975-8887 },
pages = { 81-85 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume1/number14/295-459/ },
doi = { 10.5120/295-459 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:42:15.975472+05:30
%A Rakhi Garg
%A P. K. Mishra
%T Parallel Association Rule Mining on Heterogeneous System
%J International Journal of Computer Applications
%@ 0975-8887
%V 1
%N 14
%P 81-85
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association Rule Mining from transaction–oriented databases is one of the important process that finds relation between items and plays important role in decision making. Parallel algorithms are required because of large size of the database to be mined. Most of the algorithms designed were for homogeneous system uses static load balancing technique which is far from reality. A parallel algorithm for heterogeneous system is regarded as one of the most promising platforms for association rule mining. In this paper we propose a simple parallel algorithm for association rule mining on heterogeneous system with dynamic load balancing based on Par-Maxclique algorithm. We maintain one linked list at the scheduler end that keep track of load value of every processor and each processor is having a job queue associated with it which is served in First come first basis. On the basis of load value scheduler directs the migration of task from heavy loaded to least loaded processor in the cluster during the execution and thus balances load dynamically in a cluster.

References
  1. Masahisa Tamura and Masaru Kitsuregawa, “Dynamic Load Balancing for Parallel Association Rule Mining on Heterogeneous PC Cluster System”, Proceedings of the 25th VLDB Conference, Edinburgh, Scotland, 1999, pp. 163.
  2. Mohammed J. Zaki, Srinivasan Parthasarthy, Mithsunori Ogihara and Wei Li, “Parallel Algorithms for Discovery of Association Rules”, Data Mining and knowledge Discovery, © 1997 Kluwer Academic Publishers, pp. 360, 364.
  3. M. J. Zaki, S. Parthasarathy and W. Li., “Parallel data mining for association rules on shared memory multi-processors”. In Supercomputing96, November 1996, Pittsburg, pp. 17-22.
  4. M. J. Zaki, “Parallel and Distributed Association Mining: A Survey”, IEEE, 1999, pp. 2-3, 10-13.
  5. R. Agrawal and J. Shafer, “Parallel mining association rules”, IEEE Trans. On Knowledge and Data Engineering, December 1996, 8(6): pp. 962-969.
  6. E-H. Han, G. Karypis and Vipin Kumar, “Scalable parallel data mining for association rules”, In ACM SIGMOD Conf. Management of Data, May 1997, pp. 279-284.
  7. T. Shintani and M. Kitsuregawa, “Hash based parallel algorithms for mining association rules”. In 4th Intl. conf. Parallel and Distributed Info. Systems, December 1996, pp. 20-25.
  8. D. Cheung and Y. Xiao, “Effect of data skewness in parallel mining of association rules”, in 2nd Pacific-Asia Conference on Knowledge Discovery and Data Mining, April 1998, pp. 51-55.
  9. Arun k. Pujari, A Book on Data mining techniques, Universities Press (India) Ltd., Hyderabad, 2001, First edition, pp. 94-100.
  10. Mohammed J. Zaki, “Parallel and Distributed Data Mining: An Introduction”, C.-T. Ho (Eds.): Large-Scale Parallel Data Mining, ©Springer-Verlag Berlin Heidelberg 2000. LNAI 1759, pp. 9.
  11. Kun-Ming Yu, Jiayi Zhou and Wei Chen Hsiao, “Load Balancing Approach Parallel Algorithm for Frequent Pattern Mining”, V. Malyshkin (Ed.): PaCT 2007. © Springer-Verlag Berlin Heidelberg 2007.LNCS 4671, pp. 623–631.
  12. Jochen Hipp, Ulrich G¨untzer, Gholamreza Nakhaeizadeh, “Algorithms for Association Rule Mining – A General Survey and Comparison”, SIGKDD explorations copyright © 2000, ACM SIGKDD, July 2000, Volume 2, Issue 1, pp. 58-61.
  13. Sotiris kotsiantis, Dimitris kanellopoulos, “Association Rule Mining: A Recent Overview”, GESTS International Transactions on Computer science and Engineering, Vol. 32 (1), 2006, pp. 73.
  14. Masaru Kitsuregawa and Takahilus Shintani, Masahisa Tamura and Iko Pramudiono, “Parallel Data Mining on large scale PC Cluster”, H. Lu and A. Zhou (Eds.): WAIM 2000, © Springer-Verlag Berlin Heidelberg 2000. LNCS 1846, pp. 15–26, 2000
Index Terms

Computer Science
Information Sciences

Keywords

Parallel association rule mining Heterogeneous system Par-MaxClique algorithm

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