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2018 IEEE International Conference on Big Data and Smart Computing (BigComp) (2018)
Shanghai, China
Jan 15, 2018 to Jan 17, 2018
ISSN: 2375-9356
ISBN: 978-1-5386-3649-7
pp: 163-169
ABSTRACT
Mining quantitative association rules is one of the most important tasks in data mining and exists in many realworld problems. Many researches have proved that PSO algorithm is suitable for quantitative ARM and there are many successful cases in different fields. However, the method becomes inefficient even unavailable on huge datasets. This paper proposes a parallel optimization algorithm PPQAR. The parallel algorithm designs two methods, particle-oriented and data-oriented parallelization, to fit different application scenarios. Experiments were conducted to evaluate these two methods. Results show that particle-oriented parallelization has a higher speedup, and data-oriented method is more general on large datasets.
INDEX TERMS
data mining, parallel algorithms, particle swarm optimisation
CITATION

D. Yan, X. Zhao, R. Lin and D. Bai, "PPQAR: Parallel PSO for Quantitative Association Rule Mining," 2018 IEEE International Conference on Big Data and Smart Computing (BigComp), Shanghai, China, 2018, pp. 163-169.
doi:10.1109/BigComp.2018.00032
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