隨著網際網路興起，資訊的傳播與取得隨著線上應用程式的使用頻繁，越來越簡單且快速。大量的文件與資訊在網路上流通，如何對資訊進行管理與應用變得越來越重要，其中文件自動分類的技術已被廣泛使用在新聞、搜尋引擎等網站上。過去在文件分類領域的研究，大多偏重在演算法效率的改進與分類正確率的提昇，而忽略了隨著文件不斷的增加，文件的類別會隨著有所變動，而造成原始分類類別不適用的情況。

本研究的目的在於發展以資料探勘為基礎的文件類別演進技術(MiCE)，以改善分類類別之品質。不同於Agrawal等人(1999)所提之文件類別探索之技術，本研究的文件類別演進(MiCE)技術利用文件庫中原來的分類知識，再依照類別中所包含文件的特性，以演進的方法，進行類別的分割與合併。本實證研究結果顯示，本研究所提出的文件類別演進(MiCE)技術比傳統的文件類別探索技術有更好的分類結果，可適用於不同品質的文件類別之演進，且提昇文件分類之正確率。

As text repositories grow in number and size and global connectivity improves, the amount of online information in the form of free-format text is growing extremely rapidly. In many large organizations, huge volumes of textual information are created and maintained, and there is a pressing need to support efficient and effective information retrieval, filtering, and management. Text categorization is essential to the efficient management and retrieval of documents. Past research on text categorization mainly focused on developing or adopting statistical classification or inductive learning methods for automatically discovering text categorization patterns from a training set of manually categorized documents. However, as documents accumulate, the pre-defined categories may not capture the characteristics of the documents. In this study, we proposed a mining-based category evolution (MiCE) technique to adjust the categories based on the existing categories and their associated documents. According to the empirical evaluation results, the proposed technique, MiCE, was more effective than the discovery-based category management approach, insensitive to the quality of original categories, and capable of improving classification accuracy.

TABLE OF CONTENTS I
LIST OF FIGURES II
LIST OF TABLES III
CHAPTER 1 Introduction 1
1.1 Background 1
1.2 Research Motivation and Objective 2
1.3 Organization of the Thesis 5
CHAPTER 2 Literature Review 6
2.1 Text Categorization 6
2.1.1 Preprocessing Step 7
2.1.2 Representation Step 11
2.1.3 Induction Step 14
2.2 Discovery-based Category Management Approach 28
CHAPTER 3 Mining-based Category Evolution (MiCE) Technique 29
3.1 High-level Architecture of Text Categorization with Categorization Evolution 29
3.2 Algorithm of Mining-based Category Evolution (MiCE) Technique 30
3.2.1 Category Decomposition 32
3.2.2 Category Merging 37
3.3 Complete MiCE Algorithm 40
CHAPTER 4 Evaluations of Mining-based Category Evolution 41
4.1 Test Data Set 41
4.2 Effectiveness of Category Evolution 42
4.2.1 Evaluation Procedure 43
4.2.2 Evaluation Criteria 43
4.2.3 Result 47
4.3 Sensitivity to the Quality of Categories 51
4.4 Effect of Category Evolution on Categorization Accuracy 53
4.4.1 Evaluation Procedure 53
4.4.2 Results 56
CHAPTER 5 Conclusions and Future Research Directions 57
REFERENCES 59

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