An Efficient Parallel Algorithm for Mining Frequent Pattern

Guan Xun Cui; Qian Wu; Bo He; Wei Ni

doi:10.4028/www.scientific.net/AMR.562-564.876

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 562-564An Efficient Parallel Algorithm for Mining...

An Efficient Parallel Algorithm for Mining Frequent Pattern

Abstract:

Extraction of frequent patterns in transaction-oriented database is crucial to several data mining tasks such as association rule generation, time series analysis, classification, etc. An Efficient Parallel algorithm for Mining frequent pattern (EPM) was proposed and Fast Distributed association rules Mining (FDM) algorithm was improved. Hash table technology was used to improve the generation efficiency of the 2nd candidate items . It also reduces the number of transactions in transaction database using Tid table technology. A master-slave model of parallel algorithm for mining association rules is designed in the algorithm to reduce the communication cost. The experimental results show that this algorithm has a high efficiency to deal with large database.

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Periodical:

Advanced Materials Research (Volumes 562-564)

Pages:

876-881

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.562-564.876

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Online since:

August 2012

Authors:

Guan Xun Cui, Qian Wu, Bo He, Wei Ni

Keywords:

Association Rule, Data Mining (DM), Frequent Pattern, Global Control Site, Local Site, Parallel Processing

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References

[1] AGRWAL R, SRIKAN R. Fast Algorithms for Mining Association Rules in Large Databases[C]/Proceedings of the Twentieth International Conference on Very Large Databases, Santiago, Chile 1994, 9: 487－499.

Google Scholar

[2] J.S. Park, M.S. Chen, P. S. Yu. An effective hash-based algorithm for mining association rules. in SIGMOD'95, 1995: 175–186.

DOI: 10.1145/568271.223813

Google Scholar

[3] C. Silverstein, S. Brin, R. Mowani, J. Ullman. Scalable techniques for mining causal structures. in VLDB'98, 1998: 594–605.

Google Scholar

[4] Cheung DW, Han JW, Ng WT, Tu YJ. A fast distributed algorithm for mining association rules[C]. In: Proceedings of IEEE 4th International Conference on Management of Data, Miami Beach, Florida, 1996: 31-34.

Google Scholar

[5] Wu Shao chun, Wu Geng feng, Wang Wei, Xing Dian yong. A Parallel Association Rule Algorithm for Earthquake Region-correlation[J]. Computer Applications and Software, 2006, 23(3): 20-22.

Google Scholar

[6] Yang Qi ren. An Improved Parallel Algorithm for Mining Association Rule[J]. Software Guide, 2009, 8(7): 50-52.

Google Scholar

[7] Liu Qun, Yin Yu lan. Study on Distributed Association Rules Mining Algorithm based on FDM[J]. Computer and Information Technology, 2005, 4: 18-20.

Google Scholar