利用商品金額加權關聯規則挖掘消費者適性商品

Mining Consumers’ Adaptive Products by Using Product Value Weighted Association Rules

本研究以消費者的交易資料為挖掘資料來源，每一筆交易資料包含消費者曾經購買的商品及其金額，在考量商品金額加權的情況下，利用加權關聯規則(weighted association rules)挖掘消費者適性商品。文中以k位消費者為挖掘目標，k≧1，設計一個挖掘加權關聯規則的方法，其前置項目集必須包含於k位消費者的交易資料中，根據加權關聯規則顯示的傾向特徵，可分別挖掘k位消費者適性商品。文中根據提出的方法，設計與建置一個挖掘消費者適性商品系統，本系統挖掘結果，對企業行銷人員擬訂消費者適性商品的推薦策略，並提升企業的營業收益，將可以提供非常有用的參考資訊。

This study uses customers’ transaction data as the source data of mining. Each transaction data includes the products and their values that the consumer has purchased. In the case of considering the weight of the product value, the weighted association rules are used to mine consumers’ adaptive products. Let k consumers as the target of mining, k1, and a method is designed to mine weighted association rules whose antecedents have to be contained in k consumers’ transaction data. According to the characteristics of the weighted association rules, we can find the adaptive products for k consumers. A mining system of consumers’ adaptive products is designed and constructed based on the proposed methods. It will provide very useful reference information for corporate marketers to plan recommended strategy of consumers’ adaptive products and improve corporate operating income.