Efficient Association Rule Mining Using Improved Apriori Algorithm

Association rule mining is a data mining technique to extract interesting relationships from large datasets [1, 2]. The efficiency of association rule mining algorithms has been a challenging research area in the domain of data mining [3]. Frequent pattern discovery, the task of finding sets of items that frequently occur together in a dataset is the most resource consuming phase of the rule mining process [4, 5]. Efforts for improvement, in the basic mining techniques are continuously being made. In this paper we take the classical algorithm APRIORI and optimize its performance by applying classification and sorting on the datasets.