Abstract
Association rules are very useful and interesting patterns in many data mining scenarios. Apriori algorithm is the best-known association rule algorithm. This algorithm interacts with a storage system in order to access input data and output the results. This paper shows how to optimize this algorithm adapting the underlying storage system to this problem through the usage of hints and parallel features.
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References
Christian Borgelt’s Homepage http://fuzzy.cs.uni-magdeburg.de/borgelt .
Maria S. Perez et al. A New MultiAgent Based Architecture for High Performance I/O in clusters. In Proceedings of the 2nd International Workshop on MSA’01, 2001.
Maria S. Perez et al. A Proposal for I/O Access Profiles in Parallel Data Mining Algorithms. In 3rd ACIS International Conference on SNPD’02, June 2002.
Rakesh Agrawal et al. Mining Association Rules between Sets of Items in Large Databases. In The ACM SIGMOD International Conference on Management of Data, 1993.
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© 2002 Springer-Verlag Berlin Heidelberg
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Pérez, M.S., Pons, R.A., Garcίa, F., Carretero, J., Córdoba, M.L. (2002). An Optimization of Apriori Algorithm through the Usage of Parallel I/O and Hints. In: Alpigini, J.J., Peters, J.F., Skowron, A., Zhong, N. (eds) Rough Sets and Current Trends in Computing. RSCTC 2002. Lecture Notes in Computer Science(), vol 2475. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45813-1_59
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DOI: https://doi.org/10.1007/3-540-45813-1_59
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