ScadiBino: An effective MapReduce-based association rule mining method

Current data mining algorithms are impractical for huge amounts of data because they are time consuming and therefore inefficient. Association rule mining is one of the most famous data mining algorithms. Many parallel and distributed methods have been proposed for association rule mining. However, these methods are not suited to big data for a number of reasons, such as improper data location, data skewness, lack of load balancing, lack of support for generalized association rule mining, and lack of an obvious method for rule extraction. The MapReduce-based architecture is a parallel and distributable solution for association rule mining. To improve the performance of MapReduce, proposed methods for association rules need to be customized. The performance of iterative algorithms in MapReduce architectures may not be optimum. Two main issues affect the performance of MapReduce architectures: data placement and network traffic. In this paper, a scalable and distributable binominal association rule mining method (ScaDiBino ARM) is proposed. This method converts input data items to binominal format to take advantage of scalable and distributable attributes of MapReduce structures. The proposed method was evaluated by applying it to real traffic data of a mobile operator to enable it to recommend values added services (VAS) to its customers. The results show that the rule extraction time improved significantly after applying the proposed rule mining method.