所謂挖掘關聯規則，是要從企業銷售交易資料庫中，找出項目之間的關聯性。過去研究所找出的關聯規則通常只能表達項目間有否相關，卻無法表達它們在不同購買數量時間相關性。如此所產生的問題是，我們將無法知道該以什麼的比例來搭配不同產品一齊販售。因此若關聯規則能加入項目數量資訊的話，將非常有益於制訂行銷策略。本文所提出的演算法，可以找尋出包含項目數量的關聯規則。接著利用指定項目數量的區問及模糊集合原理，找出具有語意的關聯規則。

The problem of mining association rules is to find the associations between items in a large database of sales transactions. Although there are a lot of previous researches on this area, a common problem occurred is that the rule only indicates if two items are related but as to in what quantities and in what combinations are missing. Without this information it is impossible to design a competitive combination of sales items since we didn’t know how many units of items should be included. Therefore, if the quantities of items can be included in association rules, it will be helpful for managers to make the marketing decisions. In this paper, we introduce a new algorithm for mining association rules including the quantities of items. Then, we extend the rules so that the quantities of items can be expressed as user-defined intervals or fuzzy terms.