臺灣博碩士論文加值系統

重複樣式是由一群可辨別的重複元素所組成，在現實生活中，其實有很多的應用，如在音樂序列和醫學領域的序列裡，會含有價值的重複樣式。在許多應用中重複樣式會隱藏在序列裡被視為隱性的知識，因此過去幾十年中，如何有效地獲取有價值的重複樣式一直是一個熱門議題。近幾年來，雖然提出了一些相關的研究來處理這一類的問題，但其表現仍無法獲得需要處理大量數據用戶的滿意。針對這個問題，在本文中我們提出了一個高效率的演算法，名為快速重複樣式探勘，其利用我們所設計的快速樣式索引來展現重複樣式探勘的效能。由於快速樣式索引可以顯現出每個樣式的位置資訊，因此能夠有效提供快速重複樣式的探勘。而一個給定序列只需掃描該序列一次就可以發現重複樣式，並不需要重複的掃描。最後，實驗結果也證明我們演算法的執行性能比其他的演算法還要好。

A repeating pattern is composed of identifiable elements that repeat in a predictable manner. In our real life, there are lots of applications such as musical and medical sequences containing valuable repeating patterns. The repeating patterns hidden in sequences might be viewed as implicit knowledge for identification of objects. Hence, how to efficiently and effectively retrieve the valuable repeating patterns has been a hot issue in the last decades. Although a number of studies were proposed to deal with this issue, the performance cannot still earn users'' satisfactions if large data are processed. To aim at this issue, in this thesis, we propose an efficient algorithm named Fast Mining of Repeating Patterns (FMRP) that achieves high performance of mining repeating patterns by our designed Quick-Pattern-Index (QPI). This index can provide the proposed FMRP algorithm with an effective support because it contains the information of pattern positions. Without scanning a given sequence iteratively, the repeating patterns can be discovered by only one scan of the sequence. The experimental results reveal that our proposed algorithm performs better than the compared method in finding the repeating patterns.

論文審定書 i
致謝 ii
摘要 iii
Abstract iv
Contents v
List of Figures vii
List of Tables viii
CHAPTER 1 Introduction 1
1.1 Background 1
1.2 Motivation 4
1.3 Contribution 5
1.4 Thesis Organization 5
CHAPTER 2 Related Work 7
2.1 RP-tree Approach 7
2.2 Using Suffix Tree to Search Non-Trivial Repeating Patterns 14
2.3 Applications of Repeating Patterns 17
CHAPTER 3 The Proposed Method 20
3.1 Overview 20
3.2 Illustrative Examples 24
CHAPTER 4 Empirical Study 29
4.1 Experimental Settings for Data Generations 29
4.2 Experimental Results on Different Data Settings 30
4.3 Experimental Comparisons between Suffix Tree and the Proposed Method 36
CHAPTER 5 Conclusion and Future Work 37
5.1 Conclusion 37
5.2 Future Work 38
References 39

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