隨著資訊科技與網際網路的日益發達，電子商務及知識管理的相關應用快速增加，相對的，個人與企業所需要面對的資訊量也呈現巨幅的成長，其中又以文字類型的文件為多數。為了有效管理這些數量龐大的文件，個人及企業常以單層或多層的類別將這些文件進行分類，便於日後的檢索及瀏覽，而文件分群技術也是協助管理文件的方法之一。

文件分群是一種隱含個人分群偏好的行為，每個人會依照他對這篇文章的語意認知及類別上的判斷，來進行分群。因此一個有效的文件分群技術，必須考慮每個人的分群偏好，讓分群的結果能符合個人需求，且在形式上也必須能適用於階層式的群集。然而傳統的文件分群技術主要是分析文件的內容，因此無法產生符合個人偏好的分群結果。此外現存的文件分群技術，多是產生單層的分群結果，而非多層式的階層架構。

基於上述理由，本研究發展出一種階層式的個人文件分群技術（hierarchical personalized document-clustering），簡稱HPEC。此方法不僅可依個人的分群偏好來產生他們所需要分群結果，所產生的群集形式也是階層式的。在實驗評估結果中，本研究發現HPEC在招回率上（cluster recall）比它的基準方法（HAC+P）來得優異，而在準確率（cluster precision）及距離差（location discrepancy）的表現上，也能得到相似的水平。

With the advances in information and networking technologies and the proliferation of e-commerce and knowledge management applications, individuals and organizations generate and acquire tremendous amount of online information that is typically available as textual documents. To manage the ever-increasing volume of documents, an individual or organization frequently organizes his/her documents into a set or hierarchy of categories in order to facilitate document management and subsequent information access and browsing. Furthermore, document clustering is an intentional act that reflects individual preferences with regard to the semantic coherency and relevant categorization of documents. Hence, effective document-clustering must consider individual preferences for supporting personalization in document categorization and should be capable of organizing documents into a category hierarchy. However, document-clustering research traditionally has been anchored in analyses of document content. As a consequence, most of existing document-clustering techniques are not tailored to individuals’ preferences and therefore are unable to facilitate personalization. On the other hand, existing document-clustering techniques generally are designed to generate from a document collection a set of document clusters rather than a hierarchy of document clusters. In response, we develop in this study a hierarchical personalized document-clustering (HPEC) technique that takes into account an individual’s folder hierarchy representing the individual’s categorization preferences and produces document-clusters in a hierarchical structure for the target individual. Our empirical evaluation results suggest that the proposed HPEC technique outperformed its benchmark technique (i.e., HAC+P) in cluster recall while maintaining the same level of cluster precision and location discrepancy as its benchmark technique did.

Chapter 1 Introduction 1
Chapter 2 Literature Review 5
2.1 Content-based Document-Clustering 5
2.2 Non–Content-Based and Hybrid Document-Clustering 7
2.3 Partial-Clustering-Based Personalized Document-Clustering (PEC) Technique 9
Chapter 3 Design of Hierarchical Personalized Document-Clustering (HPEC) Technique 13
3.1 Feature Extraction, Selection, and Consolidation 13
3.2 Document Representation 17
3.3 Clustering 18
Chapter 4 Empirical Evaluation 22
4.1 Data Collection 22
4.2 Evaluation Criteria 24
4.3 Tuning the Representation Scheme and the Number of Features 26
4.4 Comparative Evaluation Results 31
4.5 Sensitivity of the Size of Partial Clustering 34
4.6 Sensitivity of Cluster Size and Depth 37
Chapter 5 Conclusions 40
References 42

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