江南大学 物联网工程学院,江苏,无锡,214122
纸质出版:2017
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陈奇, 张曦煌. 基于N-list的并行频繁项集挖掘算法[J]. 微电子学与计算机, 2017, 34(5): 40-44.
CHEN Qi, ZHANG Xi-huang. An N-list based Parallel Algorithm for Mining Frequent Itemsets[J]. 2017, 34(5): 40-44.
N-list是近几年提出来的一种新的数据结构
它在频繁项集挖掘中有很高的效率.本文基于N-list提出了一种新型的并行频繁项集挖掘算法PPF算法.该算法通过扫描数据库创建一颗PPC-tree树
利用PPC-Tree树生成一系列N-list
将N-list数据表项分配到不同的节点进行深度挖掘
最后汇总所有节点的结果挖掘出所有的频繁项集.本文在四种不同的数据集上对PPF算法就行了测试和分析
实验结果表明在任何数据集上PPF算法的运行速度都是最优的.
N-list is a novel data structure proposed in recent years. It has been proven to be very efficient for mining frequent itemsets. In this paper
we present PPF
a new parallel algorithm for mining frequent itemsets. The algorithm directly scans dataset to construct a PPC-Tree. Then
the algorithm uses PPC-Tree to generate a series of N-lists which will be assigned to different nodes to mining frequent itemsets. We have conducted extensive experiments to evaluate PPF against PrePost algorithm on four various real datasets. The experimental results show that PPF algorithm is always the fastest one on all datasets.
Wei Y Q, Yang R H, Liu P Y. An improved apriori algorithm for association rules of mining[C]// IEEE International Symposium on It in Medicine % Education. Jinan, China, 2009: 942-946.
Salah S, Akbarinia R, Masseglia F. Fast parallel mining of maximally informative k-itemsets in big data[C]// IEEE International Conference on Data Mining. Atlantic City, NJ, USA, IEEE, 2015: 359-368.
Berlin Li T, Luo D. A new improved apriori algorithm based on compression matrix[M]. Berlin: Springer International Publishing, 2014: 1-15. DOI: 1
Zhong S C. Improving algorithm apriori for data mining[C]// Computational Intelligence in Decision % Control international Flins Conference. Madrid, Spain, 2015: 17-22.
Nguyen G, Le T, Vo B, et al. EIFDD: An efficient approach for erasable itemset mining of very dense datasets[J]. Applied Intelligence, 2015, 43(1): 85-94. DOI: 85
Deng Z H, Wang Z H, Jiang J J. A new algorithm for fast mining frequent itemsets using N-lists[J]. Science China Information Sciences, 2012, 55(9): 2008-2030. DOI: 2008
Grudzinski P, Wojciechowski M. Integration of candidate hash trees in concurrent processing of frequent itemset queries using Apriori. [J]. Control % Cybernetics, 2009, 38(1): 47-64. DOI: 47
Wang B L, Shen Y G. Improvement of apriori algorithm based on boolean matrix[J]. Procedia Engineering, 2011, 15(1): 1827-1831. DOI: 1827
李也白, 唐辉, 张淳, 等. 基于改进的FP-tree的频繁模式挖掘算法[J]. 计算机应用, 2011, 31(1): 101-103. DOI: 101
吴倩, 罗健旭. 压缩FP-Tree的改进搜索算法[J]. 计算机工程与设计, 2015, 35(7): 1771-1777. DOI: 1771
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