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研究生:楊昇樺
研究生(外文):Sheng-hua Yang
論文名稱:可互動調適地挖掘關聯法則之整合式架構的研究
論文名稱(外文):An Integrated Framework with Interactively and Adaptively Mining Association Rules
指導教授:邱宏彬邱宏彬引用關係
指導教授(外文):Hung-Pin Chiu
學位類別:碩士
校院名稱:南華大學
系所名稱:資訊管理學研究所
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:2003
畢業學年度:91
語文別:中文
論文頁數:70
中文關鍵詞:使用者查詢線上挖掘使用者有趣性挖掘知識發掘流程關聯法則資料挖掘稀少項目問題漸進式挖掘
外文關鍵詞:data miningonline miningincremental miningassociation rulesusers’ interests problemrare item problemKDP
相關次數:
  • 被引用被引用:1
  • 點閱點閱:134
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  • 收藏至我的研究室書目清單書目收藏:1
  過去,大部分的研究通常簡化知識發掘的過程以方便進行研究,或把挖掘過程遇到的問題分開單一討論與解決;但現實上整個程序必須整合人工處理程序及機器處理程序,而且整個知識發掘過程是一個互動且繁覆的過程,在本論文我們稱之為擴張的知識發掘模式(Intension Mining Model for KDP),在這整個過程中所遇到的問題,我們必須一併來討論及解決才有意義。我們以資料挖掘中最受到注目的關聯法則挖掘來探討,在擴張的知識發掘模式中我們進行關聯法則挖掘時會遇到各種問題,例如:漸進式挖掘、線上挖掘、使用者有趣性問題…等。而這些問題目前有非常多的研究提出解決方式,但沒有人提出能一併解決的方法,因此我們提出一新演算法QARM (Query-based Association Rule Miner)及結合我們先前所提出的MUM(Multi-layer Update Miner)演算法,設計出一整合式構架,稱之為QAMUM(Query-based Adaptive MUM)。它能針對企業或研究需要,進行一個或多個問題的解決,這使得關聯法則挖掘更具資料合用性與實用性議題。
 
  In the past, the traditional model for knowledge discovery process (KDP) was usually simplified to facilitate the proceeding of the research, or only a single sub-problem in the KDP was solved at a time. Recently, the researchers and practitioners have realized the limitations of the traditional model and felt the need for standardization of the KDP. It is significantly meaningful that all the problems in the KDP should be considered and solved together. This is so called the Intension Mining Model for KDP.
In this study, we discuss the extended model for association rule mining that is one of the popular research areas in data mining. A number of techniques and algorithms have been proposed to mine the association rules from different aspects, respectively, such as the on-line mining, the incremental mining, the interestingness problems, and so on. But few of researches have attempted to solve these problems in an integrated way. Therefore, we design a new algorithm, namely the QARM (Query-based Association Rule Miner), based on our previously proposed MUM (Multi-layer Update Miner), to construct an integrated framework, namely the QAMUM (Query-based Adaptive MUM), for mining association rules efficiently. Many experiments were conducted to verify the practicability and feasibility of the proposed approach.
 
書名頁 i
博碩士論文授權書 ii
論文指導教授推薦函 iii
論文口式合格証明 iv
中文摘要 v
英文摘要 vi
誌謝 vii
目錄 viii
表目錄 x
圖目錄 xi
 
第一章 導論 1
第一節 研究背景 1
第三節 研究動機 2
第四節 研究目的 7
第五節 研究方法 11
第六節 論文架構 13
 
第二章 相關文獻回顧 14
第一節 資料挖掘的定義 14
第二節 關聯法則挖掘演算法之相關研究 19
 
第三章 系統架構與功能 30
第一節 QAMUM系統功能 31
 
第四章 MUM演算法 34
第一節 MUM演算法之特性 34
第二節 MUM演算法之實作 37
第三節 MUM演算法的範例說明 39
第四節 MUM演算法效能之改進 43
 
第五章 QARM演算法 45
第一節 QARM演算法之特性 45
第二節 QARM之實作 50
第三節 QARM之範例說明 53
 
第六章 實驗及結果分析 57
第一節 測試環境 57
第二節 實驗設計 60
第三節 實驗結果與分析 61
 
第七章 結論與未來發展 67
 
參考文獻 68
 
表 目 錄
表2.1 資料挖掘的演化步驟 14
表4.1 MUM原始交易資料 39
表4.2 MUM主資料表與備用資料表的格式 40
表4.3 1-Itemset對應表 41
表4.4 備用資料表的變化 41
表4.5 2-Itemset對應表 42
表4.6 3-Itemset對應表 42
表4.7 4-Itemset對應表 42
表5.1 門檻值與支持度的關係與影響 49
表5.2 部份交易選取 53
表6.1 各演算法執行效率時間表 61
表6.2 漸進式挖掘時間比較表 63
表6.3 調整門檻值時間比較表 64
表6.4 遺失率與 參數的關係 66
表6.5 執行效率比較表 66
 
圖 目 錄
圖1.1 傳統的知識發掘流程模式 4
圖1.2 擴展的知識發掘流程模式 5
圖1.3 研究流程 11
圖2.1 知識發掘的架構圖 16
圖2.2 資料倉儲的架構 17
圖2.3 產生高頻項目組之範例 22
圖2.4 交易項目集絡 26
圖2.5 EDM的需求語法 28
圖3.1 QAMUM系統架構 30
圖4.1 MUM演算法步驟 37
圖4.2 交易項目拆解樹 37
圖4.3 MUM_gen虛擬碼 38
圖4.4 MUM演算法虛擬碼 38
圖5.1 母體、樣本關係圖 45
圖5.2 QARM演算法 50
圖5.3 QARM虛擬碼 51
圖5.4(a) 演算法5.2實例說明 54
圖5.4(b) 演算法5.2實例說明 55
圖5.5 演算法5.3實例說明 56
圖6.1 交易資料產生器 59
圖6.2(a) 各演算法執行效率時間 61
圖6.2(b) 各演算法執行效率時間 62
圖6.3 漸進式探勘實驗數據比較 63
圖6.4 改變門檻值時實驗數據比較 65
 
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