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研究生:林吟美
研究生(外文):Yin-mei Lin
論文名稱:在交易資料庫中利用頻繁樣式來找出離異交易之研究
論文名稱(外文):A Study of Mining Outliers from Transaction Databases by Using Frequent Patterns
指導教授:李建億李建億引用關係
指導教授(外文):Chien-i Lee
學位類別:碩士
校院名稱:國立臺南大學
系所名稱:數位學習科技學系
學門:教育學門
學類:教育科技學類
論文種類:學術論文
論文出版年:2007
畢業學年度:95
語文別:中文
論文頁數:39
中文關鍵詞:異常探勘關聯法則探勘類別資料
外文關鍵詞:Categorical dataAssociation rulesOutlier detection
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在一個資料庫裡,異常是指特性相異於其他資料的資料點。偵測出這些異常在許多應用中是很重要的。大部分傳統異常探勘的資料屬性都著重在數值屬性,但是現實生活中有很多類別屬性的資料,所以傳統的異常探勘方法就不適合使用。FindFPOF (frequent pattern outlier factor)演算法是利用頻繁項目集找出異常交易,但是FindFPOF會產生出許多頻繁樣式集。我們的目的是找出異常而不是在找出頻繁樣式,因此,本論文提出減少頻繁項目集(reduce frequent pattern set ; Rfp)來找出異常交易的方法,稱之為RfpFPOF。實驗結果顯示本論文所提出的方法在類別資料庫中發現異常交易的效能優於FindFPOF。
An outlier in a database is an observation or a point that is considerably dissimilar to or inconsistent with the remainder of the data. Outlier detection is very import for many applications. Most existing methods are designed for numeric data. They will have problems with real-life applications that contain categorical data. In order to solve this problem, some scholars propose the FindFPOF(Frequent Pattern Outlier Factor) algorithm to detect outliers from discovered frequent patterns. However, FindFPOF generates too many frequent patterns. The purpose of this study is to identify outlier not to find frequent patterns. Therefore, in this study, we present an efficient method to detect outliers by reducing the number of frequent patterns, called the reduce frequent pattern FPOF (RfpFPOF) algorithm. The experimental results have shown that RfpFPOF outperformed the FindFPOF method on identifying outliers execution time.
摘 要 I
ABSTRACT II
誌 謝 III
目錄 IV
圖目錄 V
表目錄 VI
一、緒論 1
1.1研究背景 1
1.2 研究動機與目的 2
1.3 論文架構 2
二、文獻探討 3
2.1 異常探勘 3
2.2 關聯法則探勘 5
2.3頻繁樣式基礎之離異探勘 9
三、以頻繁樣式為基礎之異常交易偵測演算法 13
3.1方法概念 13
3.2演算法分析 16
四、實驗結果與效能分析 19
4.1實驗環境與評估方式 19
4.2實驗資料集 19
4.3效能評估 20
五、結論與未來研究方向 25
5.1 結論 25
5.2 未來研究方向 25
參考文獻 26
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