時序性資料庫中未知週期之非同步週期性樣板的探勘; Mining Asynchronous Partial Periodic Pattern from Multi-event Time Series Database with Unknown Periods

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/8753

Title:	時序性資料庫中未知週期之非同步週期性樣板的探勘;Mining Asynchronous Partial Periodic Pattern from Multi-event Time Series Database with Unknown Periods
Authors:	何聰鑫;Tsung-Hsin Ho
Contributors:	資訊工程研究所
Keywords:	資料探勘;非同步週期性樣板的探勘;時間序列分析;asynchronous partial periodic pattern mining;data mining
Date:	2003-06-25
Issue Date:	2009-09-22 11:34:12 (UTC+8)
Publisher:	國立中央大學圖書館
Abstract:	目前對於週期性樣板探勘的研究均把焦點放在探勘非同步卻是單一事件樣板。然而在實際生活中，每一個時間點發生的事件數絕非單一事件樣板所能夠描述，所以，在這篇論文中，我們提出了一個嶄新的演算法，能夠廣泛地使用在實際應用上。系統有三個參數在找尋顯著樣板時被提了出來，分別是：min_rep限制每個樣板符合的顯著分割中，樣板必須不間斷地重複出現次數；max_dis規範兩兩顯著分割之間，系統所能夠忍受的雜訊間隔最大長度；total_rep指定一個樣板形成的顯著時間子序列中，樣板必須重複出現的最少次數。系統演算法由兩大部分所構成。第一部分稱為單一時間樣板探勘，在這一個部分，使用了Sliding Window方法尋找所有單一時間樣板符合的所有顯著分割。第二部分稱為樣板成長，這一部分使用BFS概念結合所有的顯著單一時間樣板，直到得到時序性資料集中所有的顯著子序列。在最後一章，實驗部分顯示我們的演算法是有效率並且穩定的。 Current research on periodic pattern mining focuses on mining asynchronous but simple single-evnet patterns. However, in real-life situation, there are more than one events happening at one time. In this paper, we propose a thoroughly-new algorithm to really solve the problem we would experience in livelihood. Three parameters min_rep, max_dis and total_rep are employed to specify the constraints a significant pattern must satisfy. Min_rep specify the minimum number of repetitions that is required within each segment of non-disrupted pattern occurrences, max_dis specify the maximum allowed disturbance between any two successive valid segments, and total_rep claims the minimum overall repetitions that is needed within a valid subsequence. Our algorithm is composed of two individual parts. One is called 1-pattern mining, and the other is called pattern growth. In the first part, a sliding window method is devised to find the entire potential valid segment matched by 1-patterns. The second part, we make use of the concept of BFS to gain valid subsequences in the overall time series dataset. Finally in experiments, our algorithm is shown efficient and stable with scale-up dataset size.
Appears in Collections:	[Graduate Institute of Computer Science and Information Engineering] Electronic Thesis & Dissertation

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