Abstract
In an effort to increase throughput for IFIN, a frequent itemsets mining algorithm, in this paper we introduce a solution, called IFIN+, for parallelizing the algorithm IFIN with shared-memory multithreads. The inspiration for our motivation is that today commodity processors’ computational power is enhanced with multi physical computational units; and therefore, exploiting full advantage of this is a potential solution for improving performance in single-machine environments. Some portions in the serial version are changed in means which increase efficiency and computational independence for convenience in designing parallel computation with Work-Pool model, be known as a good model for load balance. We conducted experiments to evaluate IFIN+ against its serial version IFIN, the well-known algorithm FP-Growth and other two state-of-the-art ones FIN and PrePost+. The experimental results show that the running time of IFIN+ is the most efficient, especially in the case of mining at different support thresholds in the same running session. Compare to its serial version, IFIN+ performance is improved significantly.
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Notes
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Swapping two nodes is simply exchanging one’s item name to that of the other.
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Huynh, V.Q.P., Küng, J., Jäger, M., Dang, T.K. (2017). IFIN+: A Parallel Incremental Frequent Itemsets Mining in Shared-Memory Environment. In: Dang, T., Wagner, R., Küng, J., Thoai, N., Takizawa, M., Neuhold, E. (eds) Future Data and Security Engineering. FDSE 2017. Lecture Notes in Computer Science(), vol 10646. Springer, Cham. https://doi.org/10.1007/978-3-319-70004-5_9
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