IPARBC: An Improved Parallel Association Rule based on MapReduce Framework

Recently, data mining in big data has become an important concern for researchers. Data mining, which refers to mining the relationship between items in a dataset, has been applied by businesses to seek profitable outcomes. Association rule mining algorithms such as Apriori and the FP-growth are efficient methods for discovering relations between items in large databases. To enhance the performance, many researches tend to enhance the traditional method using the MapReduce framework. In this paper, we proposed an improved association rule algorithm (IPARBC) based on MapReduce framework. The concept of combinatorial mathematics is used as the theoretical basis of the algorithm, and in order to improve mining performance by MapReduce framework, we address the high volume problem of big data. Experimental results show that the proposed algorithm outperform other algorithms substantially in terms of runtime.