Customized information service is very important for service provider nowadays. Traditional selective dissemination, as widely discussed in library community requires users¡¦ involvement and only serves a limited amount of users. In this thesis, we propose to employ data mining techniques to discover knowledge in circulation databases so as to provide customized service in library new book recommendation. Our research¡¦s data source is from National Sun Yat-Sen University¡¦s library. We follow a standard data mining procedure and report our experience in this thesis. Our research uses patron concept hierarchy and book hierarchy with given support threshold and confidence threshold to derived association rules with patron types being antecedent and book types being subsequent. Four algorithms, namely SBSP, SBMP, LatSBMP, MBMP are proposed to facilitate patron and book hierarchy search. Their complexities are compared analytically.

Abstract Libraries have long been widely recognized as import information-offering institutes. Thousands of new books are acquired per month by our university¡Xa mid-sized university in Taiwan), and patrons may have difficulties identifying the small set of books that really interest them. This gives rise to the problem of finding an effective way to recommend patrons the newly arrived books in a library. In this work, we address this problem in finding generalized association rules between patrons and books. We first discuss how to identify relevant but independent patron attributes in regard of the books they checked out. Then, we propose a set of algorithms for generating large itemsets and evaluate their performance experimentally. In addition, we define interestingness of rules and propose an algorithm for pruning uninteresting rules. Finally, we apply our approach to the circulation data of National SUN Yat-Sen University library and report our experiences.