Rough set theory based Apriori algorithm and its applications for sewage diagnosis

摘要

By introducing rough set theory into Apriori algorithm, a so-called RSTAA method is proposed in this paper. First, to reduce the complexity of the data, the input data are reduced according to rough set theory. Second, the relevant relationships are mined through the Apriori algorithm. At last, focused on the application of sewage diagnosis, the improved Apriori algorithm is extended in this field. To test the effectiveness of the proposed method, it is applied to the sewage data set in UCI database and a real application of a chemical plant. The experiment results show that the improved method can improve the operation speed significantly without loss of accuracy. Our proposed method could meet the requirement of real-time monitoring forecast in real applications.