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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/76348


    Title: 領域衍生式時間序列探勘技術之研究
    Other Titles: Research on Domain-Driven Time Series Data Mining Techniques
    Authors: 陳俊豪
    Contributors: 淡江大學資訊工程學系
    Keywords: Domain-Driven Data Mining;D3M;time series;data-driven data mining;time series segmentation;Actionable Knowledge Discovery;AKD
    Date: 2011-08
    Issue Date: 2012-05-07 14:55:41 (UTC+8)
    Abstract: 時間序列是指每隔一定時間將所觀察的目標記錄而成的資料。故在日常生活中, 有許多現象都可以視為時間序列資料,因此,在時間序列上亦發展了許多不同的研究 議題與成果。然而,現有的演算法皆屬於資料衍生的時間序列資料探勘技術,而其主 要缺點即是探勘後的資訊皆需經過專家進一步分析後方能運用。所以,近幾年興起了 領域衍生的資料探勘概念。可惜的是,截至目前為止,所有的時間序列探勘文獻都屬 於資料衍生的探勘技術,在領域衍生的時間序列資料探勘中,仍未見有研究進行探討。 有鑑於此,根據領域衍生的資料探勘中的四種架構,本計畫預計以兩年的時間研 發可行動知識樣式的時間序列探勘技術,並針對下列的研究議題進行探討: (1) 針對單變數時間序列,根據PA-AKD 與UI-AKD 架構,研發樣式為基礎的可行 動知識樣式時間序列切割與探勘技術。 (2) 針對多變數時間序列,根據 CM-AKD 與MSCM-AKD 架構,研發樣式為基礎 的可行動知識樣式時間序列切割與探勘技術。
    A time series is composed of lots of data points, each of which represents a value at a certain time. Many phenomena can be represented by time series. Many time series research topics and approaches have also been proposed. However, these time series mining algorithms are data-driven data mining approaches. And the main disadvantage of data-driven data mining approaches is that the derived patterns always need further analysis before they can be utilized. Thus, in recent years, a new concept “Domain-Driven Data Mining”, in short D3M, has been discussed. Unfortunately, all of the literature on time series data mining, to our best knowledge, is confined to the data-driven time series data mining environment; no research work has been conducted on domain-driven time series data mining. The aim of this project is to develop the AKD-pattern-based time series data mining methods, according to four frameworks of D3M. In this context, we will propose a two-year project, focusing on the following main issues: (1) For univariable time series data, according to PA-AKD and UI-AKD frameworks of D3M, we attempt to design AKD-pattern-based time series segmentation and mining techniques. (2) For multivariable time series data, according to CM-AKD and MSCM-AKD frameworks of D3M, we attempt to design AKD-pattern-based time series segmentation and mining techniques.
    Appears in Collections:[資訊工程學系暨研究所] 研究報告

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