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