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研究生:楊錦生
研究生(外文):Chin-Sheng Yang
論文名稱:個人化與情境感知式文件分群
論文名稱(外文):Personalized and Context-aware Document Clustering
指導教授:魏志平魏志平引用關係
指導教授(外文):Chih-Ping Wei
學位類別:博士
校院名稱:國立中山大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:英文
論文頁數:91
中文關鍵詞:文件分群個人化文件分群情境感知式文件分群文件探勘知識管理
外文關鍵詞:Context-aware document clusteringPersonalized document clusteringText miningDocument clusteringKnowledge management
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為管理日益增加的文件資料,組織與個人通常採用類別(或類別階層)的概念來整理文件,以促成文件管理之工作與協助後續文件檢索與取用之需求。文件分群是一種由分群者的個人偏好主導的意識行為,其反應的是分群者對哪些類別是適當的與文件該如何歸類的主觀認知,而此主觀意識行為會隨著分群者當時所處的情境之不同而有所差異。因此良好的文件分群技術需將個人偏好或所處情境等因素納入考量。然而,現有的文件分群技術大多僅依文件的內容來進行分群,是以無法符合個人化或情境式分群的要求。為滿足使用者對個人化與情境式分群的需求,本論文提出三個個人化或情境感知式的分群技術,並實際評估所提三個技術的效能。首先,為克服PEC技術在部份分群(Partial Clustering)過小時面臨的分群效能快速下降的問題,本文修改PEC技術並提出了Collaborative Filtering–based personalized document Clustering (CFC)技術,CFC技術採用協同推薦的概念,藉由考慮與某一使用者偏好相似的其他使用者之部份分群結果,來擴大使用者的部份分群。其次,為支援情境式文件分群,本文提出一個Context-Aware document-Clustering (CAC)技術,CAC技術考慮使用者在某一情境下的分群偏好(由一組Anchoring Terms來表達此一偏好),並利用搜尋引擎來檢索網際網路上的文件,以建構一個統計式辭典,達成情境式分群的需求。最後,CAC技術也可能面臨Anchoring Terms過少而效能快速下滑的問題,因此我們一樣採用協同推薦的概念來改善CAC技術,並提出了Collaborative Filtering-based Context-Aware document Clustering (CF-CAC)技術。根據本論文的實證評估結果,提出的三個文件分群技術在支援個人化或情境式分群時,都有相當不錯的分群效能且優於現存的文件分群技術。
To manage the ever-increasing volume of documents, organizations and individuals typically organize documents into categories (or category hierarchies) to facilitate their document management and support subsequent document retrieval and access. Document clustering is an intentional act that should reflect individuals’ preferences with regard to the semantic coherency or relevant categorization of documents and should conform to the context of a target task under investigation. Thus, effective document clustering techniques need to take into account a user’s categorization context defined by or relevant to the target task under consideration. However, existing document clustering techniques generally anchor in pure content-based analysis and therefore are not able to facilitate personalized or context-aware document clustering. In response, we design, implement and empirically evaluate three document clustering techniques capable of facilitating personalized or contextual document clustering. First, we extend an existing document clustering technique (specifically, the partial-clustering-based personalized document-clustering (PEC) approach) and propose the Collaborative Filtering–based personalized document-Clustering (CFC) technique to overcome the problem of small-sized partial clustering encountered by the PEC technique. Particularly, the CFC technique expands the size of a user’s partial clustering based on the partial clusterings of other users with similar categorization preferences. Second, to support contextual document clustering, we design and implement a Context-Aware document-Clustering (CAC) technique by taking into consideration a user’s categorization preference (i.e., a set of anchoring terms) relevant to the context of a target task and a statistical-based thesaurus constructed from the World Wide Web (WWW) via a search engine. Third, in response to the problem of small-sized set of anchoring terms which can greatly degrade the effectiveness of the CAC technique, we extend CAC and propose a Collaborative Filtering-based Context-Aware document Clustering (CF-CAC) technique. Our empirical evaluation results suggest that our proposed CFC, CAC, and CF-CAC techniques better support the need of personalized and contextual document clustering than do their benchmark techniques.
CHAPTER 1 INTRODUCTION ………………………………………………… 1
1.1 Research Background …………………………………………………… 1
1.2 Research Motivation …………………………………………………… 3
1.3 Research Objectives ……………………………………………………… 5
1.4 Organization of the Dissertation ………………………………………… 6
CHAPTER 2 LITERATURE REVIEW …………………………………………… 8
2.1 Content-based Document Clustering Techniques ……………………… 8
2.2 Non-content-based and Hybrid Document Clustering Approaches …… 10
2.3 Partial-Clustering-Based Personalized Document-Clustering Technique ... 12
CHAPTER 3 DESIGN OF COLLABORATIVE FILTERING-BASED DOCUMENT CLUSTERING (CFC) TECHNIQUE ……………………………………… 15
3.1 Collaborative Clustering-Expansion Phase …………………………… 17
3.2 Feature Construction Phase …………………………………………… 20
3.3 Document Representation Phase ……………………………………… 22
3.4 Clustering Phase ………………………………………………………… 22
CHAPTER 4 EVALUATION OF COLLABORATIVE FILTERING-BASED DOCUMENT-CLUSTERING (CFC) TECHNIQUE ……………………… 24
4.1 Data Collection ………………………………………………………… 24
4.2 Evaluation Criteria and Procedure ……………………………………… 25
4.3 Benchmark Techniques ………………………………………………… 26
4.4 Parameter Tuning ……………………………………………………… 27
4.5 Comparative Evaluation ………………………………………………… 31
4.6 Summary ……………………………………………………………… 35
CHAPTER 5 DESIGN OF CONTEXT-AWARE DOCUMENT-CLUSTERING (CAC) TECHNIQUE ………………………………………………………… 36
5.1 Feature Extraction and Selection Phase ………………………………… 37
5.2 Anchoring Term Expansion Phase ……………………………………… 37
5.3 Document Representation Phase ……………………………………… 40
5.4 Clustering Phase ………………………………………………………… 40
CHAPTER 6 EVALUATION OF CONTEXT-AWARE DOCUMENT CLUSTERING (CAC) TECHNIQUE ……………………………………… 41
6.1 Data Collection ………………………………………………………… 41
6.2 Evaluation Criteria ……………………………………………………… 42
6.3 Parameters Tuning ……………………………………………………… 43
6.4 Comparative Evaluation ………………………………………………… 45
6.5 Effects of Different Approaches for Statistical-based Thesaurus Construction …………………………………………………………… 46
6.6 Analysis of Temporal Stability of CAC ……………………………… 50
6.7 Summary ……………………………………………………………… 54
CHAPTER 7 DESIGN OF COLLABORATIVE FILTERING-BASED CONTEXT-AWARE DOCUMENT CLUSTERING (CF-CAC) TECHNIQUE ……………………………………………………………………… 56
7.1 Collaborative Context Expansion Phase ……………………………… 57
7.2 Feature Extraction and Selection Phase ………………………………… 60
7.3 Anchoring Term Expansion Phase ……………………………………… 60
7.4 Document Representation ……………………………………………… 62
7.5 Clustering Phase ………………………………………………………… 62
CHAPTER 8 EVALUATION OF COLLABORATIVE FILTERING-BASED CONTEXT-AWARE DOCUMENT CLUSTERING (CF-CAC) TECHNIQUE ……………………………………………………………………… 64
8.1 Data Collection ……………………………………………………… 64
8.2 Evaluation Criteria and Procedure …………………………………… 65
8.3 Parameter Tuning ……………………………………………………… 65
8.4 Comparative Evaluation ……………………………………………… 66
8.5 Summary ……………………………………………………………… 72
CHAPTER 9 CONCLUSION ………………………………………………… 73
9.1 Summary and Research Contributions ………………………………… 73
9.2 Future Research Directions …………………………………………… 75
REFERENCES …………………………………………………………………… 76
APPENDIX A …………………………………………………………………… 82
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