Abstract
Finding association rules is one of the most investigated fields of data mining. Computation and communication are two important factors in distributed association rule mining. In this problem Association rules are generated by first mining of frequent itemsets in distributed data. In this paper we proposed a new distributed trie-based algorithm (DTFIM) to find frequent itemsets. This algorithm is proposed for a multi-computer environment. In second phase we added an idea from FDM algorithm for candidate generation step. Experimental evaluations on different sort of distributed data show the effect of using this algorithm and adopted techniques.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
References
R. Agrawal, T. Imielinski and A. Swami. Mining association rules between sets of items in large databases. In Proc. of the ACM SIG-MOD Conference on Management of Data, 1993, pp. 207–216.
Jiawei Han, Hong Cheng, Dong Xin, Xifeng Yan, Frequent pattern mining: current status and future directions, Data Mining and Knowledge Discovery, Vol. 15, 2007, 55–86
M.S. Chen, J. Han, and P.S. Yu, “Data mining: An overview from a database perspective, IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, pp. 866–883.
J. Hipp, Ulrich Güntzer, Gholamreza Nakhaeizadeh, Algorithms for association rule mining – a general survey and comparison, ACM SIGKDD Explorations Newsletter, 2000, Vol. 2, No 1, pages 58–64
R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, in: Proceedings 1993 ACM SIGMOD Intl. Conf. on Management of Data, Washington, DC, May 1993, pp. 207–216.
Maurice Houtsma, Arun Swami, Set-oriented data mining in relational databases, Data & Knowledge Engineering, Vol. 17, No 3, December 1995, Pages 245–262
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.
H. Toivonen, T.M. Vijayaraman, A.P. Buchmann, C. Mohan, and N.L. Sarda, Sampling large databases for association rules, In Proceedings 22nd International Conference on Very Large Data Bases, 1996, pages 134–145.
S. Brin, R. Motwani, J.D. Ullman, and S. Tsur. Dynamic itemset counting and implication rules for market basket data. In Proceedings of the 1997 ACM SIGMOD International Conference on Management of Data, Vol. 26(2) of SIGMOD Record, 1997, pp. 255–264.
J. Han, J. Pie, Y. Yin and R. Mao. Mining frequent pattern without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery, 2003.
F. Bodon, “A Fast Apriori Implementation,” In B. Goethals and M. J. Zaki, editors, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Vol. 90 of CEUR Workshop Proceedings, 2003.
R. Agrawal and J. Shafer. Parallel mining of association rules. IEEE Transaction on Knowledge and Data Engineering, Vol. 8, No. 6, 1996, pp. 962–969.
D. W. Cheung, and et al., A Fast Distributed Algorithm for Mining Association Rules. In Proc. Parallel and Distributed Information Systems, IEEE CS Press, 1996, pp. 31–42.
A. Schuster and R. Wolf, Communication-Efficient Distributed Mining of Association Rules, In Proc. ACM SIGMOD International Conference on Management of Data, ACM Press, 2001, pp. 473–484.
A. Schuster, R. Wolf, and D. Trock. A High-Performance Distributed Algorithm for Mining Association Rules, Knowledge And Information Systems (KAIS) Journal, Vol.7, No. 4, 2005.
M. Z Ashrafi, D. Taniar and K. Smith, ODAM: an Optimized Distributed Association Rule Mining Algorithm, IEEE Distributed Systems Online, Vol. 5, No. 3, 2004.
F. Bodon, “Surprising Results of Trie-based FIM Algorithm,” In B. Goethals, M. J. Zaki, and R. Bayardo, editors, Proceedings of the IEEE ICDM Workshop on Frequent Itemset Mining Implementations, Vol. 90 of CEUR Workshop Proceedings, 2004.
F. Bodon, A Survey on Frequent Itemset Mining, Technical Report, Budapest University of Technology and Economic, 2006.
M. Snir, S. Otto, S. Huss-Lederman, D.Walker, J. Dongarra, MPI: The Complete Reference, The MIT Press, Cambridge, 1996
Acknowledgments
The Authors thank ITRC (Iranian Telecommunication Research Center) for their financial support. And thanks F. Alimardani for her assistance.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media B.V
About this chapter
Cite this chapter
Chelche, E.A., Dastghaibyfard, G., Sadreddini, M., Keshtakaran, M., Kaabi, H. (2009). Mining Frequent Itemsets in Distributed Environment. In: Wai, PK., Huang, X., Ao, SI. (eds) Trends in Communication Technologies and Engineering Science. Lecture Notes in Electrical Engineering, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-9532-0_22
Download citation
DOI: https://doi.org/10.1007/978-1-4020-9532-0_22
Published:
Publisher Name: Springer, Dordrecht
Print ISBN: 978-1-4020-9492-7
Online ISBN: 978-1-4020-9532-0
eBook Packages: EngineeringEngineering (R0)