隨著資訊科技與網際網路的發展，使得人們可以容易地從網路上獲取大量所需資訊（通常為文字格式的文件），因此，需要管理的電子化文件也與日遽增。在文件管理方面，傳統上人們習慣用類別的概念來整理其檔案或文件。然而，面對越來越多的電子化文件，以手動方式來管理文件類別，將耗費使用者相當多的時間、精神與體力。因此，一個能夠自動化地進行文件分群管理的工具對許多使用者來說是相當需要的。此外，每個人的分類標準與偏好往往是不盡相同的，基於自動化文件分群的需求以及個人化概念的重要性，我們提出「個人化文件分群」的技術來滿足個人在自動化文件管理上需求。為達成個人化文件分群的目的，本論文提出採用個人的部份分群(partial clustering)資訊當成擷取使用者分類偏好的來源，其中，個人的部份分群指的是由使用者提供對部分文件所作的分群結果。本研究針對文件表達方式(document representation)提出feature refinement與feature weighting二種方法，同時對分群過程提出pre-cluster-based HAC與atomic-based HAC二種方法。在以傳統的文件分群技術當作是比較的基準中，實證結果顯示本論文所提出的四種個人化文件分群技術比傳統的文件分群技術更能夠接近個人分群的結果。此外，在提出的四種個人化文件分群技術中，pre-cluster-based HAC比atomic-based HAC有較優異的分群表現；另一方面，以feature weighting方法進行的文件分群結果優於feature refinement的文件分群結果。

With the proliferation of an electronic commerce and knowledge economy environment, both organizations and individuals generate and consume a large amount of online information, typically available as textual documents. To manage the ever-increasing volume of documents, organizations and individuals typically organize their documents into categories to facilitate document management and subsequent information access and browsing. However, document grouping behaviors are intentional acts, reflecting individuals’ (or organizations’) preferential perspective on semantic coherency or relevant groupings between subjects. Thus, an effective document clustering needs to address the described preferential perspective on document grouping and support personalized document clustering. In this thesis, we designed and implemented a personalized document clustering approach by incorporating individual’s partial clustering into the document clustering process. Combining two document representation methods (i.e., feature refinement and feature weighting) with two clustering processes (i.e., pre-cluster-based and atomic-based), four personalized document clustering techniques are proposed. Using the clustering effectiveness achieved by a traditional content-based document clustering technique as performance benchmarks, our evaluation results suggest that use of partial clusters would improve the document clustering effectiveness. Moreover, the pre-cluster-based technique outperforms the atomic-based one, and the feature weighting method for document representation achieves a higher clustering effectiveness than the feature refinement method does.

CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 RESEARCH MOTIVATION AND OBJECTIVES 2
1.3 ORGANIZATION OF THE THESIS 4
CHAPTER 2 LITERATURE REVIEW 5
2.1 GENERAL PROCESS OF CONTENT-BASED DOCUMENT CLUSTERING 5
2.2 MAJOR CLUSTERING APPROACHES 8
2.3 SUPERVISED DOCUMENT CLUSTERING TECHNIQUE 11
CHAPTER 3 DESIGN OF PERSONALIZED DOCUMENT CLUSTERING 15
3.1 FEATURE EXTRACTION, SELECTION, AND CONSOLIDATION PHASE 16
3.1.1 Feature Extraction 16
3.1.2 Feature Selection 16
3.1.3 Feature Consolidation 20
3.2 DOCUMENT REPRESENTATION PHASE 21
3.3 CLUSTERING PHASE 22
3.3.1 Atomic-based HAC Method 23
3.3.2 Pre-cluster-based HAC Method 25
CHAPTER 4 EMPIRICAL EVALUATION FOR PERSONALIZED DOCUMENT CLUSTERING 28
4.1 EVALUATION DESIGN 28
4.1.1 Collection of Data Sets 28
4.1.2 Personal Categorization Data Collection 29
4.1.3 Evaluation Criteria 31
4.1.4 Evaluation Procedure 32
4.2 EVALUATION RESULT 33
4.2.1 Effect of Number of Features 33
4.2.2 Comparative Evaluation Results 38
4.2.3 Sensitivity of Size of Partial Clustering on Clustering Effectiveness 41
CHAPTER 5 CONCLUSION AND FUTURE RESEARCH DIRECTIONS 45
REFERENCES 48

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