An Optimized Association Rule Data Mining Algorithm
Yanrong Liu, Lijun Wang · 2022
Among data mining algorithms, Apriori association rule data mining algorithm is one of the most widely used algorithms. The algorithm is faced with the problems such as low accuracy of algorithm recommendation, single support and difficulty in setting the support threshold in the operation of current e-commerce recommendation systems. Based on the above problems, this paper proposes an association rule data mining algorithm combining multi-item support tree and support. The algorithm uses recursive iteration to generate conditional database and dynamically adjust the minimum support during mining to obtain frequent item sets, which greatly improves the efficiency of data mining and the accuracy of recommendation. Experiments show that the proposed association rule data mining algorithm with multi-item support greatly changes the number of association rules, thus improving the efficiency of data mining and the accuracy of recommendation.