模糊探勘程序來挖掘序列樣式

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黃正魁(Cheng-Kui Huang) 查詢紙本館藏

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資訊管理學系

論文名稱

模糊探勘程序來挖掘序列樣式
(A Fuzzy Mining Process for Discovering Sequential Patterns)

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摘要(中)

隨著資料的大量增加，資料探勘(Data Mining)已經被使用在處理資料過剩的問題，並且在既有的資料中，去挖掘有用的、新的和具有潛力的樣式。然而，我們在挖掘量化型的資料(Quantitative Data)時，卻可能產生傳統不是0就是1的切割問題(Sharp Boundary Problem)，而這問題是傳統資料探勘方法無法解決的。為了這個問題，已經有許多學者，運用模糊集合(Fuzzy Sets)去挖掘帶有數量資料的樣式，尤其是在序列樣式(Sequential Patterns)的挖掘[18][25]。為了有更一般化的觀點來看資料探勘和模糊領域的結合，而幫助去挖掘序列樣式，本研究提出了一個模糊探勘的運作程序，來引導如何挖掘序列樣式(Fuzzy Mining Process for Discovering Sequential Patterns, FMPDSP)。此程序的目的是建立一個跨兩個領域合作的橋樑，進而瞭解並分析模糊序列樣式探勘的研究步驟。另外，本研究提出了三種不同的模糊序列樣式的研究，來證明這個新程序的可行性(Workable)和其一般化(Generalization)，並引導這兩個領域結合的新研究。

摘要(英)

With the increase of data, data mining has been introduced to solve the overloading problem and to discover valid, novel, potentially useful patterns in existing data. In order to discover quantitative data, we may encounter a sharp boundary problem which the traditional data mining techniques cannot overcome. In view of this weakness, a lot of researches have been applied fuzzy sets to discover a variety of quantitative patterns, especially in sequential pattern mining [18][25]. Therefore, we devote to proposing a work process, Fuzzy Mining Process for Discovering Sequential Patterns (FMPDSP), to hold more general viewpoint combining Data Mining and Fuzzy Sets fields for discovering sequential patterns. The purpose of the process is to establish a cooperative relationship for the both fields to understand and analyze the investigating steps of fuzzy sequential pattern mining. Three researches were proposed to demonstrate that the FMPDSP can be workable and generalization to lead the future studies in the both fields.

關鍵字(中)

★ 資料探勘
★ 序列樣式
★ 模糊集合
★ 時間區間
★ 多階層
★ 數量資料

關鍵字(英)

★ multi-level
★ time interval
★ fuzzy sets
★ sequential patterns
★ data mining
★ quantitative data

論文目次

CHAPTER 1　INTRODUCTION 1
1.1　DESCRIPTION OF THE PROCESS 2
1.2　FUZZY APPLICATION 5
1.3　ORGANIZATION OF THE DISSERTATION 6
CHAPTER 2　RELATED WORKS AND BACKGROUND 8
2.1　DATA MINING 8
2.1.1　Sequential Patterns Mining 11
2.2　FUZZY SETS 15
2.3　FUZZY DATA MINING 16
CHAPTER 3　DISCOVERING FUZZY TIME-INTERVAL SEQUENTIAL PATTERNS 19
3.1　RESEARCH PROBLEM 19
3.2　PROBLEM DEFINITION 21
3.3　ALGORITHMS FOR MINING FUZZY TIME-INTERVAL SEQUENTIAL PATTERNS 25
3.3.1　The FTI-Apriori Algorithm 25
3.3.2　The FTI-PrefixSpan Algorithm 31
3.3.3　The Post-ftiapriori Algorithm 40
3.4　EXPERIMENTAL RESULTS AND PERFORMANCE STUDY 40
3.5　SUMMARY 52
3.5.1　Implications for Academic Researchers 53
3.5.2　Implications for Business Practitioners 53
3.5.3　Future Works 53
CHAPTER 4　DISCOVERING FUZZY MULTI-LEVEL SEQUENTIAL PATTERNS 54
4.1　RESEARCH PROBLEM 54
4.2　LITERATURE REVIEW FOR TAXONOMY 56
4.3　PROBLEM DEFINITION 57
4.4　ALGORITHMS FOR MINING FUZZY MULTI- AND CROSS- LEVEL SEQUENTIAL PATTERNS 66
4.4.1　Fuzzy Multi-level Sequential Mining Algorithm 66
4.4.2　Fuzzy Cross-level Sequential Patterns 73
4.5　EXPERIMENTAL RESULTS AND PERFORMANCE STUDY 76
4.5.1　Synthetic Dataset 76
4.5.2　Real Dataset 85
4.6　SUMMARY 89
4.6.1　Implications for Academic Researchers 90
4.6.2　Implications for Business Practitioners 90
4.6.3　Future Works 90
CHAPTER 5　DISCOVERING FUZZY QUANTITATIVE SEQUENTIAL PATTERNS 91
5.1　RESEARCH PROBLEM 91
5.2　PROBLEM DEFINITION 93
5.3　ALGORITHMS FOR MINING FUZZY-BASED SEQUENTIAL PATTERNS WITH QUANTITATIVE DATA 100
5.3.1　The Hong et al. Algorithm 100
5.3.2　The Divide-and-conquer Fuzzy Sequential Mining Algorithm 101
5.4　EXPERIMENTAL RESULTS AND PERFORMANCE STUDY 110
5.4.1　Synthetic Dataset 111
5.4.2　Real Dataset 120
5.5　SUMMARY 122
5.5.1　Implications for Academic Researchers 123
5.5.2　Implications for Business Practitioners 123
5.5.3　Future Works 124
CHAPTER 6　CONCLUSIONS AND FUTURE WORKS 125
REFERENCES 127
APPENDIXES 136
PUBLICATION LIST 144

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指導教授

陳彥良(Yen-Liang Chen)

審核日期

2006-6-6

推文