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Apriori is one of the key algorithms to generate frequent itemsets. Analyzingfrequent itemset is a crucial step in analysing structured data and in findingassociation relationship between items. This stands as an elementary foundationto supervised learning, which encompasses classifier and feature extractionmethods. Applying this algorithm is crucial to understand the behaviour ofstructured data. Most of the structured data in scientific domain arevoluminous. Processing such kind of data requires state of the art computingmachines. Setting up such an infrastructure is expensive. Hence a distributedenvironment such as a clustered setup is employed for tackling such scenarios.Apache Hadoop distribution is one of the cluster frameworks in distributedenvironment that helps by distributing voluminous data across a number of nodesin the framework. This paper focuses on map/reduce design and implementation ofApriori algorithm for structured data analysis.

Map / Reduce Deisgn and Implementation of Apriori Alogirthm for handling voluminous data-sets