›› 2014, Vol. 20 ›› Issue (11): 2893-2903.DOI: 10.13196/j.cims.2014.11.029
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米允龙1,2,姜麟1+,米春桥2
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Abstract: To solve the problem that a large number of candidate item sets were generated when dealing with the big data of Apriori association rules,and the negative relationships behind frequent items could not be given in big data,By combining with the principle of Boolean matrix and applying classification of rough set and MapReduce parallel programming model,an algorithm for rough association rules with negation using MapReduce was put forward to deal with negative relation of the massive data .The experimental results demonstrated that the proposed parallel algorithm was effective and fits to reveal the negative relationships behind the massive data.
Key words: data mining, rough association rules with negation, MapReduce, Apriori algorithm
摘要: 为了解决Apriori关联规则算法在处理大数据时产生大量候选项集,且无法在大数据环境下挖掘出频繁事件中所隐藏的否定关系的问题,通过深度分析事务数据库的特征,结合Boolean矩阵原理,运用粗糙集的分类思想和MapReduce并行编程模型,提出在MapReduce框架下的否定粗糙关联规则算法,以处理大数据所隐藏的否定关系。实验结果表明了该并行算法的有效性,适合挖掘出海量数据的否定关系。
关键词: 数据挖掘, 否定粗糙关联规则, MapReduce, Apriori算法
CLC Number:
TP311
TP301
米允龙,姜麟,米春桥. MapReduce环境下的否定粗糙关联规则算法[J]. 计算机集成制造系统, 2014, 20(11): 2893-2903.
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URL: http://www.cims-journal.cn/EN/10.13196/j.cims.2014.11.029
http://www.cims-journal.cn/EN/Y2014/V20/I11/2893