Hadoop Map-Reduce To Generate Frequent Item Set on Large Datasets Using Improved Apriori Algorithm

Abstract

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 item set is very fundamental part of association rule mining. Many algorithms have been proposed from last many decades including horizontal layout based techniques, vertical layout based techniques and projected layout based techniques. But most of the techniques suffer from repeated database scan, Candidate generation (Apriori Algorithms), memory consumption problem and many more for mining frequent patterns. 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.