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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李瑞庭 | |
| dc.contributor.author | Huei-Wen Wu | en |
| dc.contributor.author | 吳惠雯 | zh_TW |
| dc.date.accessioned | 2021-05-20T20:00:42Z | - |
| dc.date.available | 2013-02-04 | |
| dc.date.available | 2021-05-20T20:00:42Z | - |
| dc.date.copyright | 2010-02-04 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-01-28 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/8755 | - |
| dc.description.abstract | 近年來，探勘封閉性樣式已成為知識探索領域中重要的研究議題，其主要目的在於找出隱藏在大量資料中具有代表性的樣式。本論文針對如何從時間序列資料庫中探勘封閉性樣式，提出了三個有效率的演算法，分別為CMP (Closed Multi-sequence Patterns mining)、 CFP (Closed Flexible Patterns mining)與CNP (multi-resolution Closed Numerical Patterns mining)。
CMP演算法主要著重於多時間序列資料庫中樣式的分析，而CFP演算法可從時間序列資料庫中探勘具「彈性間隔」的封閉性樣式。CMP與CFP演算法先將時間序列轉換成符號序列，然後再探勘封閉性樣式。將時間序列轉換成符號序列可減少雜訊並簡化探勘程序，然而可能導致樣式遺失或差異很大的序列卻支持相同樣式的問題。 為避免將時間序列轉換到符號序列所造成的問題，CNP演算法可從多時間序列資料庫中直接找出具有代表性的樣式。此外，CNP演算法加入多層解析度 (multi-resolution)的概念以找出封閉性樣式，可讓使用者以不同的觀點來檢視資料。 上述三個演算法皆以深度優先探勘的方式，並配合投影資料庫以減少搜尋空間，除了加速探勘的過程外，透過有效的修剪策略及檢查封閉性的機制，可避免產生不必要的候選樣式。 本研究結果顯示CMP演算法比改良式Apriori以及BIDE演算法快了數十倍; CFP演算法比改良式Apriori演算法更有效率; CNP演算法執行速度亦較改良式A-Close演算法快。 | zh_TW |
| dc.description.abstract | Closed pattern mining is a critical research issue in the area of knowledge discovery and data mining with the aim of discovering interesting patterns hidden in a large amount of data. In this dissertation, we propose three algorithms, called CMP (Closed Multi-sequence Patterns mining), CFP (Closed Flexible Patterns mining), and CNP (multi-resolution Closed Numerical Patterns mining) to solve various issues extended from the problem of mining closed patterns.
The CMP algorithm is designed to find closed patterns in a multi-sequence time-series database. The CFP algorithm is developed to solve the problem of mining closed flexible patterns in a time-series database. Both the CMP and CFP algorithms involve a transformation of time-series sequences into symbolic sequences in the first phase. Although analyzing on symbolic sequences is ideal to reduce the effect of noises and ease the mining process, these approaches may lead to pattern lost and the sequences supporting the same pattern may look quite different. To overcome the problem raised in symbolic sequence analysis, the CNP algorithm is proposed to mine closed patterns without any transformation from time-series sequences to symbolic sequences. The method also employs the Haar wavelet transform to discover patterns in the multiple resolutions in order to provide different perspectives on datasets. All the proposed algorithms have employed the concept of projected databases to localize the pattern extension that leads to a significant runtime improvement. Moreover, effective closure checking schemes and pruning strategies are devised respectively in each of the proposed algorithms to avoid generating redundant candidates. The experimental results show that the CMP algorithm significantly outperforms the modified Apriori and BIDE algorithms. The CFP algorithm achieves better performance than the modified Apriori algorithm in all cases. And, the CNP algorithm has demonstrated a significant runtime improvement in comparison to the modified A-Close algorithm. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T20:00:42Z (GMT). No. of bitstreams: 1 ntu-99-D95725007-1.pdf: 1045780 bytes, checksum: fa4a4f8f9278bad7b959f6d7e12838a5 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | 論文摘要 i
Dissertation Abstract ii Table of Contents iv List of Figures vi List of Tables ix Chapter 1 Introduction 1 1.1 Motivations 2 1.2 Contributions 10 1.3 Dissertation layout 11 Chapter 2 Literature Review 12 2.1 Time-series mining 12 2.2 Sequential pattern mining 12 2.3 Closed pattern mining 14 2.4 SAX representation 16 2.5 Discussion 18 Chapter 3 Mining Closed Patterns in Multi-Sequence Time-Series Databases 20 3.1 Preliminaries and problem definitions 20 3.2 Frequent pattern tree 24 3.3 Closure checking and pruning strategies 25 3.4 The CMP algorithm 27 3.5 An example 31 Chapter 4 Mining Closed Flexible Patterns in Time-Series Databases 34 4.1 Preliminaries and problem definitions 34 4.2 Sequence tree 36 4.3 Support counting 37 4.4 Closure checking and pruning strategies 38 4.5 The CFP algorithm 41 4.6 An example 46 Chapter 5 Mining Multi-Resolution Closed Numerical Patterns in Multi-Sequence Time-Series Databases 48 5.1 Preliminaries and problem definitions 48 5.2 Haar wavelet transform 51 5.3 Numerical pattern tree 54 5.4 Comparison of projected databases 55 5.5 Pruning strategies 56 5.6 Frequent 1-pattern generation 58 5.7 The CNP algorithm 60 5.8 Mining multi-resolution patterns 68 5.9 An example 69 Chapter 6 Performance Evaluation 72 6.1 Synthetic data 72 6.2 Performance evaluation of the CMP algorithm 74 6.2.1 Evaluations on synthetic data 74 6.2.2 Evaluations on real data 79 6.3 Performance evaluation of the CFP algorithm 85 6.3.1 Evaluations on synthetic data 86 6.3.2 Evaluations on real data 89 6.4 Performance evaluation of the CNP algorithm 93 6.4.1 Evaluations on synthetic data 93 6.4.2 Evaluations on real data 97 Chapter 7 Conclusions and Future Work 104 References 108 | |
| dc.language.iso | en | |
| dc.title | 時間序列資料庫之封閉性樣式探勘 | zh_TW |
| dc.title | Mining Closed Patterns in Time-Series Databases | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-1 | |
| dc.description.degree | 博士 | |
| dc.contributor.oralexamcommittee | 魏志平,諶家蘭,呂永和,陳建錦 | |
| dc.subject.keyword | 資料探勘,封閉性樣式,時間序列資料庫, | zh_TW |
| dc.subject.keyword | Data mining,Closed pattern,Time-series database, | en |
| dc.relation.page | 114 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2010-01-28 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
| 顯示於系所單位： | 資訊管理學系 | |
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