臺灣博碩士論文加值系統

中文提要
圖書館一直以來為讀者提供與保存各種不同型態的重要資訊。以我們中山大學的圖書館為例，每個月新進約有上千本圖書，數量之多，使得學生讀者難於確認出真正感到興趣的新圖書。本研究旨在找出讀者族群特性知識，並應用在圖書館的新書推薦上；我們從每日的圖書借閱資料庫中挖掘出讀者與圖書間的一般化相關規則，並交由圖書館專家詮釋規則上的知識運用於新書推薦，因此我們的方法不同於專題選粹服務(SDI)，需要讀者在圖書館留下個人的喜好檔案。
本研究首先討論如何確認出與讀者圖書借閱行為有關且相互獨立的讀者屬性，再來提出三個演算法來找出large itemsets並做實驗來評量效率，除此之外，我們也訂出一套interesting rules的評量方法，最後我們報告在中山大學圖書館運用我們方法後的實際經驗。

Abstract
Libraries have long been widely recognized as import information-offering institutes. Thousands of new books are acquired per month by our university—a 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.

Contents
The Research on Finding Generalized Association Rules from Library Circulation Records 0
Abstract 5
Chapter 1 . Introduction 6
1.1. Motivation 6
1.2. Thesis Organization 7
Chapter 2 . Problem Description 8
Chapter 3 . Our Approach 13
3.1 . Identifying Relevant Patron Attributes 13
3.2 . Algorithms for Generating Large Itemsets 14
3.2.1. Problem Definition 14
3.2.2. Lattice Structure 15
3.2.3. Algorithm Basic 16
3.2.4. Algorithm K-pass 18
3.2.5. Algorithm MergePrune 20
3.3 . Identifying Interesting Rules 25
Chapter 4 . Evaluation Plan 30
4.1. Generation of Synthetic Data 30
4.2. Relative Performance of Algorithms 33
Chapter 5 . Empirical Results 39
5.1. Identifying relevant patron attributes 39
5.2. Generating patron-book rules 41
5.3. Pruning uninteresting rules 42
5.4. Effectiveness of the patron-book rules 43
Chapter 6 . Literature Review 49
6.1. SDI (Selective Dissemination of Information) 49
6.2. Recommendation Approaches 50
6.3. Data Mining Methodology 52
6.4. Clustering 53
6.5. Classification 53
6.6. Association Rules 54
Chapter 7 . Conclusion 59
7.1. Summary 59
7.2. Contributions 59
Bibliography 60
Appendix 1 63
Interesting Rules 63
Appendix 2 67
Chinese Classification Scheme 67

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