Abstract
Association Rule Mining (ARM) plays a significant role in the data mining techniques. ARM aims to reveal association relationship among different items in large datasets. The Apriori algorithm is one of the most broadly used algorithm in ARM that collects the item sets which frequently occur in order to discover association rule in massive datasets. The original Apriori algorithm is for the sequential (single node or computer) environment. This Apriori algorithm has many drawbacks to process huge datasets. Many researches have been carried out for parallelizing the Apriori algorithm. This study does a survey on few good improved and revised approaches of parallel Apriori algorithm on Hadoop- MapReduce environment. Hadoop-MapReduce framework is a programming model that efficiently and effectively processing enormous databases in parallel on large clusters of commodity hardware in a reliable, and fault- tolerant manner. This survey will provide an overall view of parallel Apriori algorithm implementation over Hadoop-MapReduce environment and briefly discussing Hadoop challenges and advantages. Keywords—ARM, Apriori, Hadoop, MapReduce.
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