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研究生:涂靖國
研究生(外文):Jing-guo Tu
論文名稱:探勘跨交易多階層關聯規則
論文名稱(外文):Mining Inter-Transaction in Multi-Level Association Rules
指導教授:黃仁鵬黃仁鵬引用關係
指導教授(外文):Jen-peng Huang
學位類別:碩士
校院名稱:南台科技大學
系所名稱:資訊管理系
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:2009
畢業學年度:97
語文別:中文
論文頁數:73
中文關鍵詞:資料探勘多層次關聯規則跨交易關聯規則過濾機制概念階層
外文關鍵詞:Data miningMultiple-Level Association RulesInter-transaction Association RulesFiltration MechanismConcept Hierarchy
相關次數:
  • 被引用被引用:1
  • 點閱點閱:270
  • 評分評分:
  • 下載下載:60
  • 收藏至我的研究室書目清單書目收藏:3
隨著資料探勘技術的日漸成熟,並且廣泛的應用在各種不同的領域上。在傳統的關聯規則中,雖然可由規則得知項目間的關係,然而,決策人員需要更多的資訊,以作出更正確的決策。在本論文中,將跨交易關聯規則中加入概念階層的概念,即結合跨交易關聯規則與多階層的概念,並提出新的演算法IML(Mining Inter-transaction in Multi-Level Association Rule)演算法。
IML演算法主要根據Apriori演算法修改而來,其流程是先掃描資料庫一次找出最高層級的頻繁項目長度一(L[1,1]),再利用L[1,1]進行前端過濾,將資料庫中各項目的最高層級有滿足最小支持度門檻的過濾,並且分別儲存項目的各層級出現位置,最後以Join、Prune、And三步驟找出各層級所有的高頻項目集。
在實驗的部分,本研究利用真實資料庫及虛擬資料庫針對每一個參數分別進行演算法的效能測試。
關鍵字:資料探勘,多層次關聯規則,跨交易關聯規則,過濾機制,概念階層
Data mining technologies are becoming more and more mature so that applied on various fields wide. On classic association rules, we can obtain the correlations between items, but decision makers still need more information from the rules to make correct decisions. Hence, in this thesis, we propose an algorithm IML(Mining Inter-transaction in Multi-Level Association Rule) which combines the concept of the Inter-transaction association rules mining and the multi-level mining.
IML algorithm is modified from association rule mining algorithm Apriori. The main process of IML is first to scan database once to find level-1 frequent 1-itemsets (L[1,1]), next to use L[1,1] and the Filtered and Pruning mechanism in earlier stage of the IML algorithm to save all level frequent 1-itemsets and the locations of them in database. And finally the IML algorithm execute “Join”, “Prune” and “And” three steps to discover all level frequent itemsets.
In the experimental, we use real datasets and synthetic datasets to test different parameters in the experimental results.
Keyword: Data mining, Multiple-Level Association Rules, Inter-transaction Association Rules, Filtration Mechanism, Concept Hierarchy
摘要 IV
Abstract V
誌謝 VI
目次 VII
表目錄 IX
圖目錄 X
第一章 緒論 1
1.1研究背景 1
1.2研究動機與目的 2
1.3研究流程 2
1.4論文架構 3
第二章 文獻探討 4
2.1資料探勘 4
2.2 關聯規則 4
2.3多層次關聯規則 9
2.4跨交易關聯規則 12
第三章 研究方法 15
3.1 IML演算法 16
3.1.1 IML演算法的流程圖 16
3.1.2 IML演算法虛擬碼 18
3.2 項目集表 19
3.3前期過濾拆解機制 20
3.4合併頻繁項目集 22
3.5修剪候選項目集 23
3.6比對候選項目集 24
3.7 IML演算法實例說明 27
第四章 實驗模擬 36
4.1 實驗環境 36
4.2實驗資料來源與格式 36
4.2.1 IBM data generator 36
4.2.2 FIMI 提供之真實資料庫 37
4.2.3資料格式內容 37
4.3 實驗設計 37
4.4 實驗結果與分析 39
4.4.1實驗測試一 39
4.4.2實驗測試二 40
4.4.3實驗測試三 41
4.4.4實驗測試四 42
4.4.5實驗測試五 43
4.4.6實驗測試六 44
4.4.7實驗測試七 45
4.4.8實驗測試八 46
4.4.9實驗測試九 47
4.4.10實驗測試十 48
4.4.11實驗測試十一 49
4.4.12實驗測試十二 50
4.4.13實驗測試十三 51
4.4.14實驗測試十四 52
4.5 實驗結論 66
第五章 結論與未來研究 68
參考文獻 71
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