An Improved Association Rule Mining Algorithm Based on the Prior Information

Abstract

Data mining can uncover valuable information from large amounts of redundant data, where association rule mining is one of the most important research element. By mining association rules, we can find connections between seemingly unrelated issues and contribute to society. However, the classic algorithms in association rule mining such as Apriori have gradually failed to complete the mining task in a short time due to the consistent growth of data. In this paper, an improved method for the association rule mining of supermarket sales based on prior information (i.e., historical data mining, and supermarket sales strategy) is proposed to address this problem. The performance of the improved association rule mining algorithm is verified by experimental studies, and the simulation results comparison and analysis have shown that the proposed method can reduce the time and space loss of mining in the mining task.