隨著文件數量的急速增加，如何有效管理知識倉儲對於知識的分享、重複使用和吸收是非常重要的。而知識地圖經常被採用來協助知識倉儲內知識的取得。通常我們是透過文件分群的方式來建立一個知識地圖。但是當今的文件分群技術並沒有辦法達到適應個人不同的偏好，也無法產生以不同觀點為基礎的知識地圖。因此，本論文提出以偏好為導向的文件分群技術，結合了使用者的偏好觀點產生其特定偏好的知識地圖。實證的結果顯示，本文所提出的方法在高cluster precision的情況之下，比傳統以內容為基礎的文件分群技術表現要好上許多。另外，與用Chi-square建立的Oracle categorizer相比，本文所提出的方法在高cluster precision時，也有較好的表現。整體而言，我們實證的結果顯示本文所提出的方法具有可行性且有高度的發展性。

Effective knowledge management of proliferating volume of documents within a knowledge repository is vital to knowledge sharing, reuse, and assimilation. In order to facilitate accesses to documents in a knowledge repository, use of a knowledge map to organize these documents represents a prevailing approach. Document clustering techniques typically are employed to produce knowledge maps. However, existing document clustering techniques are not tailored to individuals’ preferences and therefore are unable to facilitate the generation of knowledge maps from various preferential perspectives. In response, we propose the Preference-Anchored Document Clustering (PAC) technique that takes a user’s categorization preference (represented as a list of anchoring terms) into consideration to generate a knowledge map (or a set of document clusters) from this specific preferential perspective. Our empirical evaluation results show that our proposed technique outperforms the traditional content-based document clustering technique in the high cluster precision area. Furthermore, benchmarked with Oracle Categorizer, our proposed technique also achieves better clustering effectiveness in the high cluster precision area. Overall, our evaluation results demonstrate the feasibility and potential superiority of the proposed PAC technique.

CHAPTER 1 INTRODUCTION 1
1.1 Background 1
1.2 Research Motivation and Objectives 3
1.3 Organization of the Thesis 4
CHAPTER 2 LITERATURE REVIEW 6
2.1 Content-based Document Clustering Techniques 6
2.2 Non-content-based and Hybrid Document Clustering Approaches 10
CHAPTER 3 DESIGN OF PREFERENCE-ANCHORED DOCUMENT CLUSTERING (PAC) TECHNIQUE 15
3.1 Statistical-based Thesaurus Construction 17
3.2 Preference Specialization 20
3.3 Document Representation 21
3.4 Clustering 22
CHAPTER 4 EMPIRICAL EVALUATION 23
4.1 Evaluation Design 23
4.1.1 Collection of Document Corpus 23
4.1.2 Evaluation Criteria 24
4.1.3 Evaluation Procedure 25
4.2 Parameter Tuning Experiments and Results 26
4.2.1 Tuning for Traditional Content-based Document Clustering Technique 26
4.2.2 Tuning for the PAC Technique 28
4.3 Comparative Evaluation Results 33
4.4 Sensitivity to Size of Anchoring Terms 36
CHAPTER 5 CONCLUSION AND FUTURE RESEARCH DIRECTIONS 37
REFERENCES 39

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