基于上限修剪的利润集关联规则挖掘OA

Frequent itemset mining is an important method for studying user behavior.Identifying highly frequent item combina-tions in shopping basket analysis can increase item sales.However,combinations of highly frequent items do not necessarily lead to the maximum profits.Traditional frequent itemset mining algorithms overlook the quantity of items purchased by users and the differences in profits among different items.To address the profit maximization issue,this paper proposes a profit asso-ciated rule algorithm.By mining profit association rules,businesses can clearly understand which combinations of items can truly maximize profits.To enhance the efficiency of mining,the paper introduces the concept of maximum profit ratio for prun-ing candidate itemsets,which can improve the mining efficiency.The test results demonstrate that the profit association rule mining algorithm can find a set of rules that maximize profits while maintaining high mining efficiency.