Map/Reduce Design and Implementation of Apriorialgorithm for Handling Voluminous Data-Sets
Apriori is one of the key algorithms to generate frequent item-sets. Analyzing frequent item-set is a crucial step in analyzing 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 behavior 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.