期刊名称：Advanced Computing : an International Journal
印刷版ISSN：2229-726X
电子版ISSN：2229-6727
出版年度：2012
卷号：3
期号：6
DOI：10.5121/acij.2012.3604
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：Apriori is one of the key algorithms to generate frequent itemsets. Analysing frequent itemset is a crucial step in analysing structured data and in finding association relationship between items. This stands as an elementary foundation to supervised learning, which encompasses classifier and feature extraction methods. Applying this algorithm is crucial to understand the behaviour of structured data. Most of the structured data in scientific domain are voluminous. Processing such kind of data requires state of the art computing machines. Setting up such an infrastructure is expensive. Hence a distributed environment such as a clustered setup is employed for tackling such scenarios. Apache Hadoop distribution is one of the cluster frameworks in distributed environment that helps by distributing voluminous data across a number of nodes in the framework. This paper focuses on map/reduce design and implementation of Apriori algorithm for structured data analysis.
关键词：Frequent Itemset; Distributed Computing;Hadoop; Apriori; Distributed Data Mining
