河西学院学报

关联规则挖掘是数据挖掘的一个重要分支，在众多实际应用中发挥着举足轻重的作用.对关联规则的Apriori算法思想展开了深入剖析，针对该算法存在的不足之处，尝试将形式概念理论引入关联规则挖掘中，通过探讨频繁项集与概念格之间的潜在关系，提出了一种基于概念格的频繁项集生成算法.为了验证算法的有效性和优越性，以某高校大学生心理健康数据作为形式背景，开展了关联分析实验，结果表明在支持度较低的情况下，基于概念格的频繁项集生成算法展现出了显著的优势，能够更为高效、准确地挖掘出隐藏在数据中的有价值信息.

Association rule mining is an important branch of data mining and plays a crucial role in a wide range of practical applications. This paper provides an in-depth analysis of the Apriori algorithm for association rule mining and addresses its inherent limitations by introducing formal concept analysis(FCA)into the mining process. By exploring the potential relationship between frequent itemsets and concept lattices,a new frequent itemset generation algorithm based on concept lattices is proposed.To verify the effectiveness and superiority of the proposed algorithm,an association analysis experiment was conducted using college students' mental health data from a university as the formal context. The experimental results demonstrate that under lowsupport conditions,the concept lattice-based algorithm exhibits significant advantages,enabling more efficient and accurate extraction of valuable hidden information from data.