本篇論文籍由讀者之借閱資料為探勘的資料來源，每一筆借閱資料包含有讀者曾借閱過的書籍項目，利用群組(clusters)。從以下兩方面來發掘書籍最適性的推薦：一是以某一讀者為探勘的目標，並設定此一讀者之借閱資料為一群組的中心點，提出一個分群化方法，將與中心點滿足最小借閱相似度的供閱資料，歸屬於同一群組中。根據群組所顯示的借閱傾向特徵，可發掘此一讀者最適性的書籍推薦；二是以某一書籍為探勘的目標，並設定此書籍為一群組的中心點，提出一個分群化方法，將包含有此一書籍的借閱資料，歸屬於同一群組中。在此一群組中，計算出此一中心點的關聯因子，根據借閱資料與關聯因子間的借閱相似度，可發掘此一書籍做適性借閱的讀者。根據所提出的方法，設計與建置一個發掘書籍最適性的書籍推薦服務時，可提供非常有用的參考資訊。

In this paper, we use readers’ borrowing history records as the source data of mining. Each borrowing history record contains a reader ever borrowed books, and use clusters to find the most adaptive recommendations of boos from two aspects. One is to let one reader as the target of mining and assign his borrowing history record as the center of cluster. Then, we propose a clustering method to let each other borrowing history record is grouped with the center to which it contains the reader’s borrowing history record for satisfying the threshold of the minimum borrowing similarity. We can find the most adaptive book recommendations for the reader according to the characteristics of borrowing tendency of the cluster. The other is to let one book as the target of mining and assign it as the center of cluster. Then, we propose a clustering method to let each other borrowing history record is grouped with the center to which it contains the book. We compute the association factors of the center in the cluster, and find the most adaptive readers of borrowing the book according to the borrowing similarity between the association factors and borrowing history records. We design and construct a mining system for fining the most adaptive recommendations of books according to we propose the both methods. The results of the mining can provide very useful information to plan the services of the most adaptive book recommendations for libraries.