An Improvement Apriori Arithmetic Based on Rough Set Theory

Rough set theory and the association rules algorithm are mining methods which are used to find implicit rules model from large amounts of data. As the association rules mining algorithm, Apriori algorithm is gotten a lot of application used for its easy use. However, in practice, it often encountered some problem as low mining efficiency, too many invalid rules acquired and the rules of pattern mining disorder. In this paper, a algorithm called R_Apriori is designed for the problems with decision-making domain. First the conditions of the cores are mined with the rough attribute reduction algorithm, then 1-frequent item sets and the corresponding sample set is found with use mining cores set by the Apriori algorithm. And then multi- stage frequent item sets and the corresponding support and confidence can be obtained by the sample collection intersection operator. According to degree of confidence and support the corresponding strength of the rule is decided. R_Apriori algorithm solves the problems of Apriori algorithm to improve the efficiency of the algorithm and is in promotion on a certain significance.