In real-world applications, transactions usually consist of quantitative values. Many fuzzy data mining approaches have thus been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, the common problems of those approaches are that an appropriate minimum support is hard to set, and the derived rules usually expose common-sense knowledge which may not be interesting in business point of view. In this paper, an algorithm for mining fuzzy coherent rules is proposed for overcoming those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy the four criteria or not. If yes, it is a fuzzy coherent rule. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm. 在真實的應用中，交易資料通常包含數值型資料。因此，在事前設定好的最小支持度下，許多模糊資料探勘方法被提出來從數值型資料中探勘模糊關聯規則。然而，這些方法的共通問題，第一、這些方法的最小支持度不易設定；第二、所探勘出的規則只揭露常識性的資訊，使得這些規則不具有商業價值。在本論文，我們提出一個具有命題邏輯性的模糊一致性規則探勘演算法克服上述問題。他首先將數值型資料轉換成模糊集合。之後，根據產生出來的模糊集進一步產生候選模糊一致性規則。最後，計算每條候選模糊一致性規則的列聯表，並利用它來檢查規則是否有滿足命題邏輯的四個標準。如果是，該規則就是一條模糊一致性規則。在實驗部份，透過foodmart資料集一顯示所提的演算法是有效的。
