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    题名: 可行動知識發掘為基礎的模糊資料探勘技術之研究
    其它题名: Research on Actionable-Knowledge-Discovery-Based Fuzzy Data-Mining Techniques
    作者: 陳俊豪
    贡献者: 淡江大學資訊工程學系
    关键词: 料衍生式資料探勘;模糊資料探勘;領域衍生式資料探勘;遺傳模糊探勘;可行動知識規則;data-driven data mining;fuzzy data mining;Domain-Driven Data Mining (D3M);genetic-fuzzy data mining;Actionable Knowledge Rule Discovery (AKRD)
    日期: 2012-08
    上传时间: 2015-05-19 13:49:02 (UTC+8)
    摘要: 模糊資料探勘的議題主要是結合模糊理論與規則探勘,從數值型交易資料中探勘模糊關聯規則的技術。因為隸屬函數對探勘結果有一定的影響,所以,遺傳模糊探勘 技術則進一步被提出利用遺傳演算法對隸屬函數進行最佳化,並探勘模糊關聯規則。然而,現有的演算法皆屬於資料衍生的探勘技術,其主要缺點是探勘後的資訊需經過專家進一步分析後方能運用。故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,模糊資料探勘研究成果中,對於領域衍生的資料探勘技術,仍未見有研究進行探討。因此,本計畫主要目的在發展領域衍生式的模糊探勘技術。根據模糊資料探勘技術應注重的因素與領域衍生式探勘的四種架構,本計畫提出一個兩年型的研究,針對下列的研究議題進行探討:(1)針對模糊資料探勘方法,以PA-AKD架構為基礎,研發可行動知識規則的模糊探勘技術和具階層性的可行動知識規則的模糊探勘技術。(2)針對遺傳模糊資料探勘方法,以CM-AKD架構為基礎,研發可行動知識規則的遺傳模糊探勘技術和具階層性的可行動知識規則的遺傳模糊探勘技術。
    Fuzzy data mining techniques are used to discover fuzzy association rules from quantitative transactions by combining the fuzzy concepts and rule mining. Since membership functions have critical influence on the mining results, the genetic-fuzzy mining techniques are then proposed for deriving optimal membership functions and mining fuzzy association rules. However, these approaches are data-driven data mining techniques. The main disadvantage of it 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, in all of the literature on fuzzy data mining, no research work has been conducted on domain-driven fuzzy data mining. Hence the aim of this project is to develop the AKRD-based fuzzy data mining techniques. According to the general issues in designing fuzzy data mining algorithms and four frameworks of D3M, in this context, we will propose a two-year project, focusing on the following main issues: (1) For fuzzy data mining issue, according to PA-AKD framework of D3M, we attempt to design AKRD-based fuzzy data mining techniques and AKRD-based fuzzy data mining techniques with taxonomy. (2) For genetic-fuzzy data mining issue, according to CM-AKD framework of D3M, we attempt to design AKRD-based genetic-fuzzy data mining techniques and AKRD-based genetic-fuzzy data mining techniques with taxonomy.
    显示于类别:[資訊工程學系暨研究所] 研究報告

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