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基于MapReduce的高效用序列模式挖掘算法
引用本文:程思远,马超,李聪聪.基于MapReduce的高效用序列模式挖掘算法[J].计算机系统应用,2015,24(12):228-232.
作者姓名:程思远  马超  李聪聪
作者单位:复旦大学计算机科学技术学院, 上海 201203;上海市数据科学重点实验室(复旦大学), 上海 201203,复旦大学计算机科学技术学院, 上海 201203;上海市数据科学重点实验室(复旦大学), 上海 201203,复旦大学计算机科学技术学院, 上海 201203;上海市数据科学重点实验室(复旦大学), 上海 201203
摘    要:由于数据规模的快速增长,高效用序列模式挖掘算法效率严重下降.针对这种情况,提出基于MapReduce的高效用序列模式挖掘算法HusMaR.算法基于MapReduce框架,使用效用矩阵高效地生成候选项;使用随机映射策略均衡计算资源;使用基于领域的剪枝策略来防止组合爆炸.实验结果表明,在大规模数据集下,算法取得了较高的并行效率.

关 键 词:序列模式  MapReduce  剪枝策略  高效用序列模式挖掘  随机策略
收稿时间:4/7/2015 12:00:00 AM
修稿时间:2015/5/12 0:00:00

High Utility Sequential Pattern Mining Algorithm Based on MapReduce
CHENG Si-Yuan,MA Chao and LI Cong-Cong.High Utility Sequential Pattern Mining Algorithm Based on MapReduce[J].Computer Systems& Applications,2015,24(12):228-232.
Authors:CHENG Si-Yuan  MA Chao and LI Cong-Cong
Affiliation:School of Computer Science, Fudan University, Shanghai 201203, China;Shanghai Key Laboratory of Data Science, Fudan University, Shanghai 201203, China,School of Computer Science, Fudan University, Shanghai 201203, China;Shanghai Key Laboratory of Data Science, Fudan University, Shanghai 201203, China and School of Computer Science, Fudan University, Shanghai 201203, China;Shanghai Key Laboratory of Data Science, Fudan University, Shanghai 201203, China
Abstract:Because of the rapid growth of data, the high utility sequential pattern mining algorithms' efficiency decreases seriously. In view of this, we propose a high utility sequential pattern mining algorithm based on MapReduce, namely HusMaR. This algorithm is based on MapReduce, which using the utility matrix to generate candidate efficiently, random mapping strategy to balance of computing resources and field-based pruning strategy to prevent an explosion. Experimental results show that in the large scale of data, the algorithm achieves a high parallel efficiency.
Keywords:sequential pattern  MapReduce  pruning strategy  high utility sequential pattern mining  random strategy
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