Abstract
Parallel computing is very important in providing the computing speed and scalability needed for large scale data mining applications. In order to achieve a good performance, a good scheduling of parallel tasks is very important. This paper proposes and evaluates various scheduling strategies for parallel FI-growth data mining. We show that the execution time of parallel data mining on multicore cluster systems depends on a task scheduling strategy used. Using simulation, we compare 9 strategies on 8 to 64 core multicore cluster systems. The results show that selecting the right strategy can substantially reduce the execution time of parallel data mining on multicore cluster systems.
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Benjamas, N., Uthayopas, P. (2009). An Impact of Scheduling Strategy to Parallel FI-Growth Data Mining Algorithm. In: Papasratorn, B., Chutimaskul, W., Porkaew, K., Vanijja, V. (eds) Advances in Information Technology. IAIT 2009. Communications in Computer and Information Science, vol 55. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10392-6_5
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DOI: https://doi.org/10.1007/978-3-642-10392-6_5
Publisher Name: Springer, Berlin, Heidelberg
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