ABSTRACT

Association rule discovery from large databases is one of the tedious tasks in datamining.The process of frequent itemset mining, the first step in the mining of association rules, is a computational and IO intensive process necessitating repeated passes over the entire database. Sampling has been often suggested as an effective tool to reduce the size of the dataset operated at some cost to accuracy. Data mining literature presents with numerous sampling based approaches to speed up the process of Association Rule Mining(ARM).Sampling is one of the important and popular data reduction technique that is used to mine huge volume of data efficiently. Sampling can speed up the mining of association rules. In this paper, we provide an overview of existing sampling based association rule mining algorithms.