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。過去大部份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的相關性，也忽略掉每項商品會因其利潤大小帶來不同重要性。真實世界往往是兩種資料都記錄的，而傳統關聯規則方法卻只使用了部份的資料來推導規則，這意味著我們只能得出部份的資訊來創造出部份的價值。如此所產生的問題是，我們將無法知道該以什麼樣的比例來搭配不同產品一齊販售，也無法得知該項搭配是否能為公司帶來利潤。因此若關聯規則能加入項目數量及利潤資訊的話，將非常有益於制訂行銷策略。 本文提出，以權重值大小表示為該筆交易紀錄之重要性，其中，於單筆交易紀錄中，每賣出一樣商品所獲得的利潤作為該筆交易紀錄的權重值，取代傳統關聯規則方法只在意商品有否被購買的想法，並依購買數量出現次數將商品作分割可以找尋出包含項目數量的關聯規則，本篇研究將利用指定項目數量的區間以權重的方式找出更具有意義的關聯規則。 ;Association Rule is an important type of knowledge representation revealing implicit relationships among the items present in large number of transactions. The traditional association rules mining apply binary execution. It cares about the attendance and absence of items in the transaction all along. Recent research shows that traditional mining method is not so realistic and it might be lost some important patterns. The patterns include the information from profit and purchased quantity of items that would also cause the meaning of transaction records are the same. In our study, according to the different profit and purchased quantity of items in the transaction, the importance of each record should be different. We are going to modify Apriori Algorithm into non-binary way with weights. Which emphasizes the importance of the quantity, we use the separation methods to divide items into segmentations. Since the usage of the ignored data, we receive more information in detail with the results of the Quantity-based association rules. These rules bring the information that includes not only the occurrence relationship of the items but also the profit relationship for the business. We get the more specific relationship with the purchased situation than before.
