Mining quantitative association rules involves identifying the relationships between items based on their purchased quantities from a transaction database. In general, items with varying quantities are treated as distinct new items, making it challenging to meet the criteria for these new items. Previous methods have often relaxed these criteria to uncover quantitative association rules; however, excessive relaxation can lead to the discovery of many irrelevant rules, while insufficient relaxation may result in few or no rules being found. Additionally, the quantity ranges associated with items can be divided into intervals, and some of these intervals need to be combined, potentially leading to information loss during the process. In this article, we present new definitions and an approach for discovering quantitative association rules that address the issues of user-defined criteria, range segmentation, and interval integration, allowing us to identify the quantitative association rules that genuinely interest users.