A Parallel Algorithm for Mining onshelf Utility Itemset with Negative Item Values

doi:10.3969/j.issn.10062475.2018.04.003

Abstract

Abstract: In order to improve the mining efficiency of the onshelf utility itemset mining algorithms with negative item values, the paper proposed a parallel algorithm for mining onshelf utility itemset with negative item values named DTPHoun (distributed TPHoun algorithm). Based on MapReduce, the algorithm divides the database according to the onshelf time periods. The algorithm transforms the mining work into MapReduce job, the Map phase to mine candidates in database fragments, and the Reduce phase to calculate the onshelf utility values of the candidates in parallel. The experimental results show that the DTPHoun algorithm has a good performance.

Key words: utility itemset mining, onshelf time periods, MapReduce, negative item values

CLC Number:

TP311.13

CHEN Lijuan, XIE Huosheng. A Parallel Algorithm for Mining onshelf Utility Itemset with Negative Item Values[J]. Computer and Modernization, doi: 10.3969/j.issn.10062475.2018.04.003.

References

［1］ Liu Junqiang, Wang Ke, Fung B C M. Mining high utility patterns in one phase without generating candidates［J］. IEEE Transactions on Knowledge and Data Engineering, 2016,28(5):12451257.
［2］ Jin Jue, Wang Shui. Rup/Frupgrowth: An efficient algorithm for mining high utility itemsets［J］. Procedia Engineering, 2017,174:895903.
［3］ Krishnamoorthy S. Pruning strategies for mining high utility itemsets［J］. Expert Systems with Applications, 2015,42(5):23712381.
［4］ Zida S, FournierViger P, Lin Chunwei, et al. EFIM: A fast and memory efficient algorithm for highutility itemset mining［J］. Knowledge & Information Systems, 2017,51(2):595625.
［5］ Chu C J, Tseng V S, Liang T. An efficient algorithm for mining high utility itemsets with negative item values in large databases［J］. Applied Mathematics and Computation, 2009,215(2):767778.
［6］〖KG-*2〗〖ZK(#〗FournierViger P. FHN: Efficient mining of highutility itemsets with negative unit profits［C］// Proceedings of the 10th International Conference on Advanced Data Mining and Applications. 2014:1629.
［7］ Lin Chunwei, FournierViger P, Gan Wensheng. FHN: An efficient algorithm for mining highutility itemsets with negative unit profits［J］. KnowledgeBased Systems, 2016,111:283298.
［8］ Lan Guocheng, Hong T P, Tseng V S. Discovery of high utility itemsets from onshelf time periods of products［J］. Expert Systems with Applications, 2011,38(5):58515857.
［9］ Lan Guocheng, Hong T P, Huang J P, et al. Onshelf utility mining with negative item values［J］. Expert Systems with Applications, 2014,41(7):34503459.
［10］FournierViger P, Zida S. FOSHU: Faster onshelf high utility itemset mining with or without negative unit profit［C］// Proceedings of the 30th Annual ACM Symposium on Applied Computing. 2015:857864.
［11］李伟卫,赵航,张阳,等. 基于MapReduce的海量数据挖掘技术研究［J］. 计算机工程与应用, 2013,49(20):112117.
［12］AlHamodi A A G, Lu Songfeng. MapReduce frequent itemsets for mining association rules［C］// Proceedings of the 2016 International Conference on Information System and Artificial Intelligence. 2016:281284.
［13］〖JP3〗董新华,李瑞轩,周湾湾,等. Hadoop系统性能优化与功能增强综述［J］. 计算机研究与发展, 2013,50(S2):115. 
［14］杜江,张铮,张杰鑫,等. MapReduce并行编程模型研究综述［J］. 计算机科学, 2015,42(S1):537541.
［15］宋杰,孙宗哲,毛克明,等. MapReduce大数据处理平台与算法研究进展［J］. 软件学报, 2017,28(3):514543.
［16］Liu Ying, Liao Weikeng, Choudhary A N. A twophase algorithm for fast discovery of high utility itemsets［C］// Proceedings of the 9th PacificAsia Conference on Advances in Knowledge Discovery and Data Mining. 2005:689695.
［17］〖JP3〗FournierViger P, Gomariz A, Lam H, et al. SPMF: Opensource Data Mining Platform［EB/OL］. http://www.philippefournierviger.com/spmf, 20170920.

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