Abstract
This paper addresses the problem of association rules mining with large scale data sets using bees behaviors. The bees swarm optimization method have been successfully running on small and medium data size. Nevertheless, when dealing with large benchmark, it is bluntly blocked. Additionally, graphic processor units are massively threaded providing highly intensive computing and very usable by the optimization research community. The parallelization of such method on GPU architecture can be deal large data sets as the case of WebDocs in real time. In this paper, the evaluation process of the solutions is parallelized. Experimental results reveal that the suggested method outperforms the sequential version at the order of ×70 in most data sets, furthermore, the WebDocs benchmark is handled with less than forty hours.
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References
Han, J., Kamber, M., Pei, J.: Data mining: concepts and techniques. Morgan Kaufmann (2006)
Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. ACM SIGMOD Record 22(2) (1993)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Record 29(2) (2000)
Kuo, R.J., Chao, C.M., Chiu, Y.T.: Application of particle swarm optimization to association rule mining. Applied Soft Computing 11(1), 326–336 (2011)
Djenouri, Y., Drias, H., Habbas, Z., Mosteghanemi, H.: Bees Swarm Optimization for Web Association Rule Mining. In: 2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology (WI-IAT), vol. 3, pp. 142–146. IEEE (2012)
Djenouri, Y., Drias, H., Chemchem, A.: A hybrid Bees Swarm Optimization and Tabu Search algorithm for Association rule mining. In: 2013 World Congress on Nature and Biologically Inspired Computing (NaBIC). IEEE (2013)
Moslehi, P., et al.: Multi-objective Numeric Association Rules Mining via Ant Colony Optimization for Continuous Domains without Specifying Minimum Support and Minimum Confidence. International Journal of Computer Science (2008)
Agrawal, R., Shafer, J.C.: Parallel mining of association rules. IEEE Transactions on Knowledge and Data Engineering 8(6) (1996)
Parthasarathy, S., et al.: Parallel data mining for association rules on shared-memory systems. Knowledge and Information Systems 3(1) (2001)
Fang, W., et al.: Frequent itemset mining on graphics processors. In: Proceedings of the Fifth International Workshop on Data Management on New Hardware. ACM (2009)
Zhou, J., Yu, K.-M., Wu, B.-C.: Parallel frequent patterns mining algorithm on GPU. In: IEEE International Conference on Systems Man and Cybernetics (SMC). IEEE (2010)
Adil, S.H., Qamar, S.: Implementation of association rule mining using CUDA. In: International Conference on Emerging Technologies, ICET 2009. IEEE (2009)
Cui, Q., Guo, X.: Research on Parallel Association Rules Mining on GPU. In: Yang, Y., Ma, M. (eds.) Proceedings of the 2nd International Conference on Green Communications and Networks. LNEE, vol. 224, pp. 215–222. Springer, Heidelberg (2012)
Zhang, F., Zhang, Y., Bakos, J.: Gpapriori: Gpu-accelerated frequent itemset mining. In: IEEE International Conference on Cluster Computing (CLUSTER). IEEE (2011)
Li, H., Zhang, N.: Mining maximal frequent itemsets on graphics processors. In: Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol. 3. IEEE (2010)
Guvenir, H.A., Uysal, I.: Bilkent university function approximation repository, 2012-03-12 (2000), http://funapp.cs.bilkent.edu.tr/DataSets
Goethals, B., Zaki, M.J.: Frequent itemset mining implementations repository. This site contains a wide-variety of algorithms for mining frequent, closed, and maximal itemsets (2003), http://fimi.cs.helsinki.fi
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Djenouri, Y., Drias, H. (2014). Parallel Bees Swarm Optimization for Association Rules Mining Using GPU Architecture. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_7
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DOI: https://doi.org/10.1007/978-3-319-11897-0_7
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