因為文字資料庫的內容和數量愈來愈多，使得文字挖掘成為在知識探索領域中快速成長的一個應用。過去相關的文字資料探勘技術的研究，主要都著重在找出文件本身內所含有的樣板(patterns)，如文件分類、文件分群、查詢展開(query expansion)和事件追蹤等技術，而找出文件與文件之間關連的相關研究則非常缺乏。本研究的目的在於從次序性的文件中，找出文件與文件之間的關連，我們將這種關連稱作事件演化關連(evolution patterns)，並提出事件演化關聯之探勘技術。透過發掘出的事件演化關連，我們可用來支援環境掃描(environmental scanning)和知識管理 (knowledge management)，並且可用來輔助文件管理(document management)和擷取(document retrieval)的技術，譬如可用來輔助事件追蹤。

Due to the ever-increasing volume of textual documents, text mining is a rapidly growing application of knowledge discovery in databases. Past text mining techniques predominately concentrated on discovering intra-document patterns from textual documents, such as text categorization, document clustering, query expansion, and event tracking. Mining inter-document patterns from textual documents has been largely ignored in the literature. This research focuses on discovering inter-document patterns, called evolution patterns, from document-sequences and proposed the evolution pattern discovery (EPD) technique for mining evolution patterns from a set of ordered sequences of documents. The discovery of evolution patterns can be applied in such domains as environmental scanning and knowledge management, and can be used to facilitate existing document management and retrieval techniques (e.g., event tracking).

Chapter 1. Introduction....................................................1
1.1 Background.............................................................1
1.2 Research Motivation....................................................2
1.3 Research Objective.....................................................5
1.4 Organization of the Thesis.............................................5

Chapter 2. Problem Analysis and Definition.................................7
2.1 Characteristics of Document-sequences..................................7
2.2 Definition of Evolution Patterns......................................10
2.3 Requirements for Mining Feature-based Evolution Patterns..............13

Chapter 3. Literature Review..............................................18
3.1 Temporal Patterns Discovery Algorithm.................................18
3.2 Document Clustering...................................................22
3.2.1 Preprocessing Phase.................................................23
3.2.2 Document Representation Phase.......................................24
3.2.3 Clustering Phase....................................................24

Chapter 4. Evolution Pattern Discovery Technique..........................27
4.1 Process of Evolution Patterns Discovery (EPD) Technique...............27
4.2 Example...............................................................32
4.3 Algorithm of Evolution Patterns Discovery (EPD) Technique.............36

Chapter 5. Empirical Evaluation...........................................37
5.1 Evaluation Application: Event Tracking................................37
5.1.1 Traditional Event Tracking Technique................................37
5.1.2 Event Tracking Techniques Supported by Evolution Patterns...........39
5.2 Parameter Tuning Experiments..........................................44
5.2.1 Data Set............................................................45
5.2.2 Evaluation Criteria.................................................46
5.2.3 Parameter Tuning Procedure..........................................49
5.2.4 Evaluation Results for Parameters Tuning Experiments................49
5.3 Empirical Evaluation for Supporting Event Tracking....................56
5.3.1 Data Set............................................................56
5.3.2 Evaluation Criterion................................................57
5.3.3 Evaluation Procedure................................................58
5.3.4 Comparative Evaluation..............................................59

Chapter 6. Conclusions and Future Research Directions.....................63

References................................................................65

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