文章基本信息

标题：An Enhanced Frequent Pattern Growth Based on MapReduce for Mining Association Rules
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作者：ARKAN A. G. AL-HAMODI ; SONGFENG LU ; YAHYA E. A. AL-SALHI 等
期刊名称：International Journal of Data Mining & Knowledge Management Process
印刷版ISSN：2231-007X
电子版ISSN：2230-9608
出版年度：2016
卷号：6
期号：2
页码：19
DOI：10.5121/ijdkp.2016.6202
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：In mining frequent itemsets, one of most important algorithm is FP-growth. FP-growth proposes analgorithm to compress information needed for mining frequent itemsets in FP-tree and recursivelyconstructs FP-trees to find all frequent itemsets. In this paper, we propose the EFP-growth (enhanced FPgrowth)algorithm to achieve the quality of FP-growth. Our proposed method implemented the EFPGrowthbased on MapReduce framework using Hadoop approach. New method has high achievingperformance compared with the basic FP-Growth. The EFP-growth it can work with the large datasets todiscovery frequent patterns in a transaction database. Based on our method, the execution time underdifferent minimum supports is decreased..
关键词：Association Rule; frequent pattern; Mapreduce; Hadoop.