臺灣博碩士論文加值系統

專家學者發現海洋的鹽度與溫度在全球氣候變遷中佔有極重要角色。海水溫度與鹽度不正常變化研究已引起人們廣泛注意與討論。我們希望用資料探勘技術找出Argo海水鹽度、溫度變化的關聯性。過去相關的研究都只以文字表達資料探勘的結果。若能將文字的探勘結果轉成視覺化方式表達，對未來災害預防將有進一步幫助。
傳統關聯法則演算法在尋找關聯法則時，僅能找出交易中項目與項目間的關係，則視覺化效果就會受影響。因此我們採用FITI技術於量化跨界性關聯法則探勘模型，如此能有效地從被轉化後的Argo資料中找出具時空變化關係之溫度與鹽度樣式。例如：當臺灣北部地區海水鹽度上升0.1psu到0.2psu，則我們推測下個月臺灣東北部地區的溫度將會上升0℃到0.8℃。此溫度及鹽度變化樣式最大特色為其樣式不僅有溫度及鹽度間變化關係，更含有其時間及空間變化關係資訊於其中。
本研究收集並分析台灣附近海域海水溫度與鹽度資料。將海水鹽度、溫度相關時間、空間樣式先行找出來，再結合視覺化工具呈現本文之研究結果。

The domain experts find that ocean salinity and temperature play an important role in global climate changes. Global ocean salinity and temperature abnormal variations attract researchers to find interesting patterns. Data mining strategy is used to discover association rules from Argo ocean salinity and temperature variations. In the past, the association rules are only described in rule forms. A visualization system is constructed to help users observe the association rules and their variations. In our research, the ocean salinity and temperature variation data along the Taiwan coast were analyzed.
Traditional mining techniques focus on finding associations among items within one transaction. They are unable to discover rich contextual patterns related to location and time. FITI algorithm is used to find the association rules. The quantitative inter-transaction association rules mining algorithm is proposed to find the salinity and temperature abnormal variation patterns from the transformed data set. Example from the discovered association rules looks like, “If the salinity near the northern Taiwan rose 0.1psu to 0.2psu, then the temperature near the northeast Taiwan will rise from 0℃ to 0.8℃ in the next month.”
This study focuses on ocean salinity and temperature variations obtained from the waters surrounding Taiwan. A visualization system is constructed for users to easily understand inter-transaction association rules from ocean salinity and temperature variations.

ACKNOWLEDGMENT iii
ENGLISH ABSTRACT iii
CHINESE ABSTRACT iv
TABLE OF CONTENTS v
LIST OF FIGURES vii
LIST OF TABLES ix
CHAPTER 1 INTRODUCTION 1
1.1 Motivation 1
1.2 The Problem Definitions 3
1.3 Objective 4
1.4 Thesis Organization 5
CHAPTER 2 RELATED WORK 6
2.1 Data Mining 6
2.2 Temporal Sequence Pattern Mining 20
2.3 Spatial Data Mining 23
2.4 Inter-Transaction Association Rules 30
CHAPTER 3 SYSTEM DESIGN AND ARCHITECTURE 34
3.1 System Architecture 34
3.2 Argo Data 38
3.3 Defining Transactions in Argo Data 41
3.4 Mining Spatial-Temporal Patterns 42
CHAPTER 4 SYSTEM IMPLEMENTATION 46
4.1 System Implementation 46
4.2 Experimental Results and Analysis 51
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 63
REFERENCES 64

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