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研究生:林思甄
研究生(外文):Szu-Chen Lin
論文名稱:針對非同步週期性樣式探勘之新資料結構及演算法
論文名稱(外文):The New Data Structure and Algorithm for Mining Asynchronous Periodic Patterns
指導教授:葉介山葉介山引用關係
指導教授(外文):Jieh-Shan Yeh
學位類別:碩士
校院名稱:靜宜大學
系所名稱:資訊管理學系研究所
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
論文出版年:2008
畢業學年度:97
語文別:中文
論文頁數:61
中文關鍵詞:週期性樣式探勘非同步性序列樣式探勘
外文關鍵詞:sequential pattern miningperiodic patterns miningasynchronous
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所謂的週期性樣式探勘是指在時間相關的資料集中,搜尋含有週期性的有用樣式。有別於早期的此類研究多著重於發掘同步週期性樣式 (synchronous periodic patterns),近年來非同步週期性樣式之探勘 (asynchronous periodic patterns) 漸漸受到重視,目前已有許多方法被提出,但這些演算法對於干擾因子的處理,亦還忽略一些有用的資訊。因此,本論文的主要目的是對非同步週期性樣式探勘,提出一更全面性、有效樣式能更被充分利用的方法。首先,我們提出了OEOP演算法針對單一事件找出所有有用的資訊,再參考Huang及Chang學者 [9] 所提出的非同步週期性樣式探勘一般化模式,將運用OEOP方法所找出的有效單一樣式,結合成為多重事件之單一樣式、多重事件之多重樣式和非同步週期性樣式。
此外,我們亦將本篇中所提出的方法,運用在實際的週期性資料中,如:蛋白質序列GenBank和加權股價指數序列,及人工產生之含多重事件樣式的序列,以觀察演算法中各變數間的關係及執行效能,結果顯示變數間都能維持穩定且正確的關係,亦有不錯的執行效能。
The periodic pattern mining is to discover valid periodic patterns in a time-related dataset. Previous studies often have considered synchronous periodic patterns. However, asynchronous periodic patterns mining gradual received more and more attention recently. There are many methods that have been proposed for the periodic patterns mining in literature. But those algorithms for dealing with disturbances may neglect some valid patterns. Therefore, the aim of this paper is to offer a more general method of mining asynchronous periodic patterns and to generate all valid periodic patterns. First, we propose OEOP algorithm to discover all kinds of valid segments in each single event sequence. Then, refer to the general model of asynchronous periodic patterns mining proposed by Huang and Chang, we combine these valid segments found by OEOP algorithm into 1-patterns with multiple events, multiple patterns with multiple events and asynchronous periodic patterns. Besides, we implement these algorithms on three real and one synthetic periodic datasets. Then test the relationships of each variable and the efficiency of algorithms. The relationship of the input parameters and the efficiency of algorithms are also examined. The experimental results show that these algorithms have the good performance and scalability.
中文摘要 i
英文摘要 ii
誌 謝 iii
目錄 iv
表目錄 vi
圖目錄 vii
一、緒論 1
1.1 背景 1
1.2 動機與目的 2
二、相關研究 4
2.1 序列樣式探勘 4
2.1.1 序列樣式探勘的基本定義 4
2.1.2 序列樣式探勘的方法 5
2.2 同步週期性樣式探勘 10
2.2.1 同步週期性樣式探勘的基本定義 10
2.2.2 同步週期性樣式探勘的方法 11
2.3 非同步週期性樣式探勘 15
2.3.1 非同步週期性樣式探勘的基本定義 15
2.3.2 非同步週期性樣式探勘的方法 17
三、研究方法 25
3.1 資料過濾與轉換 27
3.2 單一週期樣式探勘 28
3.2.1 OEOP (One Event One Pattern Mining) 28
3.2.2 OEOP v.s. HBV 33
3.3 多重事件之週期性樣式探勘 34
3.3.1 MEOP (Multiple Events One Pattern Mining) 34
3.3.2 MEMP (Multiple Events Multiple Patterns Mining) 37
3.4非同步週期性樣式探勘 39
四、實驗 41
4.1 實驗資料 41
4.1.1 GenBank序列 41
4.1.2 股價序列 42
4.1.3 人工生成之多重事件序列 43
4.1.4 實驗序列探勘結果 43
4.2 實驗結果 45
4.2.2 MEOP和MEMP實驗結果 48
4.2.3 ASPM實驗結果 49
五、結論 51
5.1 結論 51
5.2 未來研究方向 51
參考文獻 52
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[9]K. Huang and C. Chang, "SMCA: A General Model for Mining Asynchronous Periodic Patterns in Temporal Databases," IEEE Transactions on Knowledge and Data Engineering, vol.17, no.6, pp. 774-785, 2005.
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