Abstract: Association rule mining is a classic data mining method, and more and more companies regard it as an indispensable strategic analysis tool. The current association rule mining method has too many rules, which makes it difficult for users to understand in the application. Therefore, the research on the reduction method of the association rule set has application value. This paper studies the influence of the prime attributes contained in the keywords of the database schema on the association rules generated by the association rule mining based on the Apriori algorithm. It is found that partial functional dependence will cause the frequent appearance of redundant information in the data set of association rule mining and produce no practical value. Recognition and elimination of such rules can realize the reduction of the rule set. As the same with finding all the candidate keywords, finding all the prime attributes is an NP problem. Therefore, this paper studies a verification method based on a candidate keyword to determine the prime attributes, so as to design and implement the association rule mining reduction algorithm based on the determination of the prime attributes. Finally, the effectiveness of the method is ve- rified in the experiment. 

Key words: data mining, association rule, prime attribute, association relationship, algorithm optimization