大量文字型態的線上資訊隨著各種電子商務的應用而產生，組織或個人也不斷地從各種資訊管道獲取必要的新資訊。為了管理及存取的方便性，組織或個人常建立適當的文件類別對既有的文件檔案進行組織與管理。由於網路的快速及便利，組織或個人很容易地從網路上獲取得大量的資訊文件。倘若收集得的文件集合中含有來源處給予文件之類別分類方式的資訊，故如何運用文件集合中既有的分類資訊，快速、準確地將集合中的文件整合至組織或個人既有的類別目錄中則成為本研究的研究重點。回顧既有的文獻，已有學者利用分類技術發展出分類式類別整合法(例如，加強式貝氏分類器，ENB)用以提升上述類別整合工作的效率，然而分類式類別整合法必須在如下列的特定條件下方能有效地運作：新文件集合與既有的類別目錄間必須有著同質性的文件分類方式、既有的類別目錄內必須存有相當數量的已分類文件等。這些特定條件的限制造成類別整合法應用上的限制，因此本研究利用群集技術發展了一個類別整合方法，稱之為群集式類別整合法(Clustering-based Category Integration technique, CCI)，用以放寬上述的條件限制、增加可應用的範圍。根據實證評估的結果顯示，不論新文件集合與既有類別目錄間的分類方式屬於相同、類似或完全不同的情況下，CCI所達成的整合準確度均優於ENB；而CCI對於既有類別目錄內文件數的需求也遠低於ENB。
隨著文件數量的成長，用以管理文件的類別數量也將隨之增加，組織或個人通常將文件類別以樹狀的階層結構予以組織。由於CCI方法並不考慮類別目錄或文件集合內的類別間的階層結構關係，因此本研究同時發展了群集式的階層類別整合法(Clustering-based category-Hierarchy Integration technique, CHI)，用以處理階層式文件類別之整合。實驗結果顯示，當整合兩個階層式的文件類別時，若其分類方式屬於同質或類似的情況下，CHI可以有效地提昇文件類別整合的準確度。

E-commerce applications generate and consume tremendous amount of online information that is typically available as textual documents. Observations of textual document management practices by organizations or individuals suggest the popularity of using categories (or category hierarchies) to organize, archive and access documents. On the other hand, an organization (or individual) also constantly acquires new documents from various Internet sources. Consequently, integration of relevant categorized documents into existent categories of the organization (or individual) becomes an important issue in the e-commerce era. Existing categorization-based approach for document-category integration (specifically, the Enhanced Naïve Bayes classifier) incurs several limitations, including homogeneous assumption on categorization schemes used by master and source catalogs and requirement for a large-sized master categories as training data. In this study, we developed a Clustering-based Category Integration (CCI) technique to deal with integrating two document catalogs each of which is organized non-hierarchically (i.e., in a flat set). Using the Enhanced Naïve Bayes classifier as benchmarks, the empirical evaluation results showed that the proposed CCI technique appeared to improve the effectiveness of document-category integration accuracy in different integration scenarios and seemed to be less sensitive to the size of master categories than the categorization-based approach.
Furthermore, to integrate the document categories that are organized hierarchically, we proposed a Clustering-based category-Hierarchy Integration (referred to as CHI) technique extended the CCI technique and for category-hierarchy integration. The empirical evaluation results showed that the CHI technique appeared to improve the effectiveness of hierarchical document-category integration than that attained by CCI under homogeneous and comparable scenarios.

1. INTRODUCTION 1
1.1 Background 1
1.2 Analysis of Category Integration Problem 3
1.3 Research Motivation and Objectives 5
1.4 Organization of the Dissertation 8
2. LITERATURE REVIEW 9
2.1 Naïve Bayes Classification for Category Integration 9
2.2 Document Clustering 12
2.2.1 Feature extraction and selection Phase 13
2.2.2 Document Representation Phase 14
2.2.3 Clustering Phase 15
3. DESIGN OF CLUSTERING-BASED CATEGORY INTEGRATION TECHNIQUE 19
3.1 Overall Process of CCI 19
3.2 Extraction of Categorization Scheme 21
3.3 Source Category Decomposition 23
3.4 Category Merging 26
3.5 New Category Generation 30
4. EVALUATION OF CLUSTERING-BASED CATEGORY INTEGRATION (CCI) TECHNIQUE 33
4.1 Collection of Document Corpora 33
4.2 Types of Evaluation 34
4.3 Evaluation Design and Results: Source-document Assignment Task 36
4.3.1 Creation of Synthetic Catalogs 36
4.3.2 Evaluation Procedure and Criteria 39
4.3.3 Evaluation Results for Complete Coverage Integration 40
4.3.3.1 Parameter Tuning for ENB 40
4.3.3.2 Parameter Tuning Experiments for CCI 46
4.3.3.3 Comparative Evaluation Results 49
4.3.3.4 Effect of Data Size on Classification Accuracy 50
4.3.4 Evaluation Results for Partial Coverage Integration 55
4.3.4.1 Parameter Tuning for ENB 57
4.3.4.2 Parameter Tuning for CCI 61
4.3.4.3 Comparative Evaluation Results 63
4.3.4.4 Effect of Data Size on Classification Accuracy 65
4.4 Evaluation Design and Results: New Category Generation Task 69
4.4.1 Creation of Synthetic Catalogs 69
4.4.2 Evaluation Criteria 70
4.4.3 Evaluation Results for New Category Generation 71
5. DESIGN OF CLUSTERING-BASED CATEGORY-HIERARCHY INTEGRATION TECHNIQUE 76
5.1 Overall Process of CHI 76
5.1 Extraction of Hierarchical Categorization Schemes 78
5.2 Selection of Merging Target 80
5.3 Category-hierarchy Merging 80
6. EVALUATION OF CLUSTERING-BASED CATEGORY-HIERARCHY INTEGRATION (CHI) TECHNIQUE 86
6.1 Collection of Category Hierarchy and Document Corpus 86
6.2 Evaluation Design 87
6.2.1 Creation of Synthetic Category Hierarchies 87
6.2.2 Evaluation Procedure and Criteria 93
6.3 Evaluation Results 94
7. CONTRIBUTIONS AND FUTURE RESEARCH 97
7.1 Contributions 97
7.2 Future Research Directions 98
REFERENCES 101

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