Variable precision rough set model based dataset partition and association rule mining

Discovery of association rules is one of the most important tasks in data mining. Many efficient algorithms have been proposed in the literature. In this paper, a method of dataset-partitioning using conceptual hierarchy and a variable precision rough set model is presented. An algorithm for mining association rules using this technique is designed, and an asynchronous algorithm is proposed, too. The efficiency of the algorithm and the factors that affect the efficiency of the algorithm are analyzed by mining association rules in a dataset artificially generated. The result of an experiment proves the efficiency of the algorithm.