台灣連鎖藥通路已逐漸轉型為複合式藥局，除了購買處方藥以外，現今藥局銷售商品種類眾多且逐漸成為社區商店之型態，讓民眾一次性購足藥品、化妝品、食品及生活日用品等。顧客於門市消費後累積了大筆的顧客會員銷售資料，本研究結合大數據資料採礦之技術，應用在顧客購買行為與行銷策略之間的相應關係，並藉此了解顧客在藥局通路的消費型態，進而衍生出符合顧客需求的行銷組合方案。 本研究藉由台灣某家連鎖藥局的銷售時點情報系統(POINT OF SALE, POS)資料分析顧客會員之購買行為，依據會員之購買日期、購買品項、購買金額等，應用資料採礦分析方法，先利用RFM模型分析顧客價值群的特性概況，再利用APRIORI演算法針對該連鎖藥局的八大類別商品銷售資料探討顧客會員購買產品的關聯規則，依照結果衍生出不同的商品銷售組合，並在門市執行有效的行銷策略以提升營業額。最後，依據研究結果對該家連鎖藥局提供銷售的策略及建議，作為該連鎖藥局業者後續經營之參考。 / The development of the domestic pharmacies in Taiwan has influenced by the government policy and logistics. Gradually, the traditional pharmacy had been replaced by the chain pharmacies to face the demands of products variety, customized and the consumer information. The members' purchase behaviors were analyzed in this study through the Point of Sale(POS) data from chain pharmacy headquarters. The purchase behaviors of the pharmacy members were analyzed based on purchase date, purchase item, amount of purchase etc. First, data regarding customer purchasing records are collected and widely known RFM model is used to evaluate the value for each customer. Second, an association rule mining tool, the Apriori algorithm, is used to analyze the relationship between customer purchasing records and products, to obtain hidden and useful purchasing rules for each product category. The association rules obtained can help the decision managers plan their new cross-selling strategies for products in future.