Survey Paper for Enhancement of Apriori Algorithm

As with the advancement of the IT technologies, the amount of accumulated data is also increasing. It has resulted in large amount of data stored in databases, warehouses and other repositories. Thus the Data mining comes into picture to explore and analyze the databases to extract the interesting and previously unknown patterns and rules known as association rule mining. 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. 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.