
系統識別號 
U00261910201005493300 
論文名稱(中文) 
時間序列前處理與樣式探勘技術 
論文名稱(英文) 
Time Series Data Preprocessing and Pattern Mining Techniques 
校院名稱 
成功大學 
系所名稱(中) 
資訊工程學系碩博士班 
系所名稱(英) 
Institute of Computer Science and Information Engineering 
學年度 
99 
學期 
1 
出版年 
99 
研究生(中文) 
余謝輝 
研究生(英文) 
HsiehHui Yu 
學號 
p7891118 
學位類別 
博士 
語文別 
英文 
論文頁數 
92頁 
口試委員 
指導教授曾新穆 口試委員洪宗貝 召集委員李建億 口試委員謝孫源 口試委員林威成 口試委員高宏宇 口試委員陳俊豪

中文關鍵字 
資料探勘
資料前處理
資料精簡
資料離散化
遺傳演算法
分群
時間序列分析

英文關鍵字 
data mining
data preprocessing
data reduction
data discretization
genetic algorithm
clustering
time series analysis

學科別分類 

中文摘要 
近年來，由於資訊的爆炸性發展，巨量的資料愈易取得，人們對於如何從中獲取有用的知識也顯得愈形迫切，造就了更多資料探勘技術被廣泛的發展及應用於資料的分析上。這些挖掘出來的資訊就像是所羅門王的寶藏一樣，在各領域，如商業戰略、社群分析、科學探索及醫學研究上都顯得相當珍貴。然而，真實世界的資料，因為大量而廣泛的來源，造成了許多的雜訊和不一致性。這些低品質的資料就像是一張模糊的藏寶圖， 探索家永遠不可能找到正確的寶藏位置。因此，如何改善資料的品質，藉以提升資料探勘結果的可信度就變成了一個很大的挑戰。另外，如何從特殊形式的資料庫(例如具有高維度與少量樣本)中探勘出有用的樣式也一直是一個重要議題。
在本論文中，針對時間序列資料庫和交易資料庫，我們分別提出了幾個資料前處理的方法來提升資料分析及樣式探勘的準確性。對於時間序列資料庫而言，我們提出了兩種資料離散化方法並應用於樣式的探勘。第一種方為以切割技術為基礎之資料離散方法，此方法結合了分群技術與遺傳演算法，可以自動的產生樣式。第二種方法，稱為PIPSAX，是一種結合精簡化與符號化概念的資料離散化方法。我們利用這個方法來做顯露式樣式（Emerging Patterns）的探勘。上述兩個方法，透過真實財金資料的實驗分析與驗證，的確可提供實用且有效的分析結果。
此外，對於擁有極高維度且少量樣本的特殊資料，我們則提了出一個可結合專業知識庫與多階層資訊精簡技術的方法。所提出的方法具有下列兩個優點，第一點為：所提出的方法可以利用不同階層順序同時考慮多種不同的科學算術標準。第二點為：透過多元知識庫的整合，提供使用者更具有相關知識根據的分析結果。

英文摘要 
In recent year, data mining techniques have been extensively used in data analysis due to the wide availability of huge amounts of data and imminent need for mining useful information from such data. The useful information is like King Solomon’s treasure which can be greatly helpful in many fields, such as business strategies, society analysis, scientific exploration, medical research and etc. However, realworld databases are highly susceptible to noisy and inconsistent due to the heterogeneous sources and huge size. Lowquality data is just like a blurring treasure map, explorers can never find the correct site. How to improve the quality of data and enhance the mining results thus presents a challenge. In addition, how to mine interesting patterns from the special case of transaction databases (i.e. data with small samples and large number of features) is also a critical issue.
In this dissertation, some effective preprocessing approaches are proposed for data analysis and pattern discovery. We address the issue on two types of data, timeseries databases and transaction databases.
For the time series databases, we propose two discretization methods for pattern discovery. The first one is a segmentationbased discretization method, which combines the clustering techniques and genetic algorithm to derive patterns automatically. The second one is a reduction and symbolizationbased discretization method, namely PIPSAX. The proposed method is utilized in Emerging Patterns (EPs) mining. Experimental results on real financial dataset also show the effectiveness of the two proposed approaches.
Moreover, for the special case of transaction databases which has small samples and large number of features, a novel multiinformationbased data reduction approach hybrid with a knowledgebased data integration approach is proposed. The proposed method has two main advantages. The first one is that the proposed approach can take multicriterion into consideration in different order. The second one is that the proposed approach can get better results by integrating with heterogeneous knowledge databases.

論文目次 
摘 要 I
ABSTRACT II
ACKNOWLEDGEMENT IV
LIST OF FIGURES VIII
LIST OF TABLES IX
CHAPTER 1 INTRODUCTION 1
1.1 MOTIVATION 1
1.2 OVERVIEW OF THE DISSERTATION 3
1.2.1 Time Series Segmentation and Pattern Discovery Approaches 3
1.2.2 The PIPSAXBased Emerging Pattern Mining Approach 4
1.2.3 A MultiInformationBased Gene Scoring Approach with Biological Knowledge 5
1.3 ORGANIZATION OF THE DISSERTATION 5
CHAPTER 2 TIME SERIES SEGMENTATION AND PATTERN DISCOVERY APPROACHES 6
2.1 INTRODUCTION 6
2.2 REVIEW OF DIMENSION REDUCTION APPROACHES 8
2.2.1 Discrete Wavelet Transformation 9
2.2.2 Perceptually Important Points (PIP) 10
2.3 REVIEW OF CLUSTER VALIDATION TECHNIQUES 11
2.4 REVIEW OF TIME SERIES SEGMENTATION APPROACHES 13
2.5 GENETIC COMPONENTS OF THE PREVIOUS APPROACH 15
2.5.1 Chromosome Representation and Initial Population 15
2.5.2 Clustering Segments into Groups 16
2.5.3 Fitness and Selection 17
2.5.4 Genetic Operators 19
2.6 THE PROPOSED PIPBASED TIME SERIES SEGMENTATION AND PATTERN DISCOVERY ALGORITHM 19
2.6.1 Incorporating Perceptually Important Points (PIP) Approach 19
2.6.2 Enhanced Suitability Factor for Chromosome Evaluation 20
2.6.3 The Proposed Evolutionary Algorithm for PIPbased TimeSeries Segmentation and Pattern Discovery 23
2.7 THE PROPOSED GRANULARITYBASED TIME SERIES SEGMENTATION AND PATTERN DISCOVERY ALGORITHM 24
2.7.1 Incorporating Granular Computing Concept into Pattern Discovery 25
2.7.2 Improved Fitness and Selection 28
2.7.3 The Proposed Algorithm 31
2.8 EXPERIMENTAL RESULTS OF THE FIRST PROPOSED METHOD 33
2.8.1 The Efficiency of the Proposed Approach 34
2.8.2 Comparison of the Proposed and the Previous Approaches 36
2.8.3 The Impact of the Improved Suitability Factor 38
2.9 EXPERIMENTAL RESULTS OF THE SECOND PROPOSED METHOD 39
2.9.1 The Effectiveness of the Proposed Approach 39
2.9.2 Comparison of the Proposed and Previous approaches 42
2.10 SUMMARY 44
CHAPTER 3 THE PIPSAXBASED EMERGING PATTERN MINING APPROACH 47
3.1 INTRODUCTION 47
3.2 RELATED WORK 49
3.2.1 Symbolic Aggregative Approximation (SAX) 50
3.2.2 Perceptually Important Points (PIPs) 51
3.2.3 Mining Approaches for Emerging Patterns (EPs) 52
3.3 PROBLEM DEFINITION 53
3.4 THE PROPOSED APPROACH FOR MINING TIME SERIES EPS 55
3.4.1 The Proposed Framework 55
3.4.2 Data Transformation 56
3.4.3 EPs Mining Algorithm: TSEPsMiner 56
3.5 EXPERIMENTAL EVALUATION 59
3.5.1 Experimental Datasets 59
3.5.2 Number of Discovered EPs 60
3.5.3 Average Length of Discovered EPs 63
3.5.4 Average Growth Rate of Discovered EPs 64
3.6 SUMMARY 64
CHAPTER 4 A MULTIINFORMATIONBASED GENE SCORING APPROACH WITH BIOLOGICAL KNOWLEDGE 66
4.1 INTRODUCTION 66
4.2 REVIEW OF PROPOSED FRAMEWORK 68
4.2.1 General Preprocessing Module 69
4.2.2 MultiInformationBased Gene Scoring Method with Biological Knowledge Module 69
4.2.3 Classification Module 71
4.3 MULTIINFORMATIONBASED GENE SCORING METHOD WITH BIOLOGICAL KNOWLEDGE 71
4.3.1 Gaussian Overlap Approach 71
4.3.2 Transformation of Gene Expression Values 71
4.3.3 Transformation of Biological Knowledge 72
4.3.4 Threshold Number of Misclassification (TNoM) Approach 74
4.4 EXPERIMENTAL RESULTS 75
4.4.1 Estimation of TNoM Score 76
4.4.2 Accuracy of Classification 78
4.4.3 Decision Tree Rules 79
4.4.4 The Most Informative GO Terms 79
4.5 SUMMARY 82
CHAPTER 5 CONCLUSIONS AND FUTURE WORK 83
BIBLIOGRAPHY 86

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