A Distributed Association Rules Mining Algorithm

Abstract:

Data mining has attracted a great deal of attention in the information industry in recent years and can be used for applications rangning from business management, production control, and science exploration etc. Most of the existing data mining algorithms are processing in the centralized systems; however, at present large database is usually distributed. Compared with the frequent itemsets lost and high communication traffic in distributed database conventional and improved algorithm FDM, An improved distributed data mining algorithm LTDM based on association roles is proposed. LTDM algorithm introduces the mapping indicated array mechanism to keep the integrity of frequent itemsets and decrease the communication traffic. The experimental results prove the efficiency of the proposed algorithm. The algorithm can be applied to information retrieval and so on in the digital library.

• Periodical: Advanced Materials Research (Volumes 971-973)
• Pages: 1459-1462
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.971-973.1459
• Online since: June 2014
• Authors: Wen Liang Cao*, Li Ping Chen
• Keywords: Association Rule, Communication Traffic, Distributed Database, Frequent Itemsets

References

[1] M. Hahsler, G. Bettina, K. Hornik, and C. Buchta, Introduction to arules a computational environment for mining association rules and frequent item sets, (2010).

DOI: 10.18637/jss.v014.i15

[2] P. K. Novak, N. Lavra and G. I. Webb. Supervised descriptive rule discovery: A unifying survey of contrast set, emerging pattern and subgroup mining. Journal of Machine Learning Research, 10, (2009).

[3] Yang Ming, Sun Zhihui, Jigen Lin. Fast mining global frequent itemsets [J]. Computer Research and Development, 2013, 40 (4) : 620-626.

[4] Agrawal, R., Imielinski, T., Swami, A., Mining Association Rules between Sets of Items in Large Databases (1993) ACM SIGMOD Conf. Management of Data.

DOI: 10.1145/170035.170072

[5] Wang Chunhua, Huang houkuang, Li honglian. A fast and efficient algorithm for distributed multi mining association rules [J]. Computer Research and Development, 2010, 38 (4) : 438-443.

[6] D.W. Cheung, J. Han, V. Ng, A.W.C. Fu, and Y. Fu. A Fast Distributed Algorithm for Mining Association Rules. Proc. 1996 Int'l Conf. Parallel and Distributed Information Systems (PDIS '96), pp.31-42, (1996).

DOI: 10.1109/pdis.1996.568665

[7] S. Nijssen, T. Guns, and L. D. Raedt. Correlated itemset mining in roc space: a constraint programming approach. In KDD, pages 647-656, (2009).

DOI: 10.1145/1557019.1557092