企業運用資訊技術可以快速儲存消費者的交易資料，這些交易資料記錄消費者曾經購買的產品，若能從這些大量交易資料中深入分析消費者的購買行為，將產品傳達給最有興趣的消費者，對提升企業經營必定可以提供相當大的幫助。本研究以消費者的交易資料為探勘資料來源，每一筆交易資料記錄消費者曾經購買的產品項目，以k 個產品為探勘目標，k≥1，以產品為中心修改資料探勘中的PAM 演算法，設計一個分群化交易資料成k 個群組的方法，且分群化後之k 個群組的產品相似度總和為最大，然後分別從各群組中找出目標產品與其他產品之間關聯性，做為判斷k 個產品適性消費者的依據。分群化過程中除了保留PAM 演算法的精神，取代原先中心點的交易資料也能具備各目標產品的群組獨特性，刪除未包含k 個任一產品的交易資料，可提升後續分群化計算的執行效能。文中根據提出的方法，設計與建置一個發掘產品適性消費者探勘系統，其探勘結果對企業規劃產品適性消費者，可以提供相當有用的參考資訊。

Enterprises use information technology to quickly store consumers’ transaction data. These transaction data record the products that consumers have purchased. If we can analyze consumers’ purchase behavior from these large amounts of transaction data, and communicate products to the most interested consumers, it can certainly provide considerable benefits to enhance business operations. This paper uses consumers’ transaction data as the source data of mining, and each transaction data contains the product items that a consumer has purchased. Let k products as the target of mining, k1. Considering the products as the center, we modify the PAM algorithm to present a clustering method to cluster transaction data to k groups with the maximum similarity of products. The association between other products and the target products are found from each group, and as the basis for judging adaptive consumers of k individual products. In addition to keep the spirit of the PAM algorithm in the process of clustering, replacing the transaction data of the original center points also have the groups uniqueness with the target products. Deleting transaction data that does not contain any of k products can improve the efficiency of subsequent clustering computations. A mining system of finding adaptive consumers of products is designed and built according to the proposed methods. The results of mining can provide very useful information to plan adaptive consumers of products marketing.