Implementation of Improved Apriori Algorithm on Large Dataset using Hadoop

Abstract

The association rule of data mining is an elementary topic in mining of data. Association rule mining discovery frequent patterns, associations, correlations, or fundamental structures along with sets of items or objects in transaction databases, relational databases, and other information repositories. The amount of data increasing significantly as the data generated by day-to-day activities. In data mining, Association rule mining becomes one of the important tasks of descriptive technique which can be defined as discovering meaningful patterns from large collection of data. Mining frequent itemset is very fundamental part of association rule mining. As in retailer industry many transactional databases contain same set of transactions many times, to apply this thought, in this thesis present an improved Apriori algorithm that guarantee the better performance than classical Apriori algorithm. Compare existing system and proposed system on the basis of execution time and memory. Found that proposed system taking less time and memory compare to existing system.