文章基本信息

标题：Clustering of Association Rules for Big Datasets using Hadoop MapReduce
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作者：Salahadin A. Moahmmed ; Mohamed A. Alasow ; El-Sayed M. El-Alfy 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：3
页码：536-545
DOI：10.14569/IJACSA.2021.0120364
出版社：Science and Information Society (SAI)
摘要：Mining association rules is essential in the discovery of knowledge hidden in datasets. There are many efficient association rule mining algorithms. However, they may suffer from generating large number of rules when applied to big datasets. Large number of rules makes knowledge discovery a daunting task because too many rules are difficult to understand, interpret or visualize. To reduce the number of discovered rules, researchers proposed approaches, such as rules pruning, summarizing, or clustering. For the flourishing field of big data and Internet-of-Things (IoT), more effective solutions are crucial to cope with the rapid evolution of data. In this paper, we are proposing a novel parallel association rule clustering approach which is based on Hadoop MapReduce. We ran many experiments to study the performance of the proposed approach, and promising results have been demonstrated, e.g. the lowest scaleup was 77%.
关键词：Internet of Things; big data mining; clustering; association rules; Hadoop