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研究生:邱麗如
研究生(外文):Li-Ju Chiu
論文名稱:以布林表格為基礎的資料挖掘方法及其應用
論文名稱(外文):The Method of Data Mining Based on Boolean Table and Its Applications
指導教授:蔡英德蔡英德引用關係
指導教授(外文):Yin-Te Tsai
學位類別:碩士
校院名稱:靜宜大學
系所名稱:資訊管理學系研究所
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:2005/07/
畢業學年度:93
語文別:英文
論文頁數:80
中文關鍵詞:資料挖掘關聯挖掘布林表格網路選課
外文關鍵詞:Online Course EnrollmentBoolean TableData MiningAssociation Mining
相關次數:
  • 被引用被引用:5
  • 點閱點閱:142
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本研究根據知識發現流程(KDD) 及 Apriori 演算法提出我們的挖掘程序,同時以布林表格為資料結構。 以布林表格為基礎僅需在 Apriori 演算法之第一回合時掃描資料庫(讀取資料至布林表格),其餘回合再以布林表格為資料來源,故可節省掃描資料庫的時間。在挖掘程序中本研究只須運用兩個功能:
(1) 產生候選項目集(Candidate Itemsets) 及依 Apriori 定理作修剪。
(2) 將產生的高頻項目集( Frequent Itemsets)由多筆轉為單筆,供產生下回合之 候選項目集(Candidte itemsets) 使用。
我們將本研究所提的方法與其它演算法進行時間及空間上的效能比較,發現本研究方法有不錯的表現。
The thesis proposed an association rules mining algorithm based on KDD (Knowledge discovery in Database) methodology and Apriority algorithm scanning the full database for every pass during its mining process. The proposed algorithm scanned transactions in database only in the first pass and converted them into Boolean Table data structure used for subsequent processing. No further scanning was needed afterwards and hence I/O time was reduced substantially. Two key processes of the proposed algorithm were:(1) Generate candidate item sets and prune them by Apriori principle.(2) Convert and combine multiple records of frequent item sets into single record o generate new candidate item sets in the next pass.
The performance of the proposed algorithm was compared to other related algorithms in terms of time and space and results were discussed in details.
中文提要 …………………………………………………………………… i
英文提要 …………………………………………………………………… ii
誌謝 …………………………………………………………………… iii
目錄 …………………………………………………………………… iv
表目錄 …………………………………………………………………… vi
圖目錄 …………………………………………………………………… vii
第一章 緒論……………………………………………………………… 1
1.1 料挖掘概述……………………………………………………… 1
1.1.1 資料挖掘(Data Mining)定義…………………………………… 2
1.1.2 資料挖掘技術…………………………………………………… 3
1.1.3 資料挖掘限制…………………………………………………… 9
1.1.4 資料挖掘未來挑戰……………………………………………… 10
1.2 研究動機與目的………………………………………………… 10
1.3 論文架構………………………………………………………… 11
第二章 文獻探討………………………………………………………… 12
2.1 關聯法則(Association Rule)探討………………………………… 12
2.1.1 關聯法則的意義………………………………………………… 12
2.1.2 關聯法則之相關名詞…………………………………………… 13
2.1.3 關聯法則之推導………………………………………………… 13
2.1.4 關聯法則之探討………………………………………………… 15
2.1.5 關聯法則之相關應用…………………………………………… 16
2.2 Apriori 演算法探討……………………………………………… 18
2.2.1 演算法…………………………………………………………… 18
2.2.2 演算法的改良: AprioriTid……………………………………… 24
2.3 DHP(Direct Hashing and Pruning) 演算法探討………………… 27
2.4 DLG演算法探討………………………………………………… 29
2.5 Boolean Algorithm 探討………………………………………… 32
2.6 網路選課資料挖掘探討………………………………………… 37
第三章 研究方法………………………………………………………… 38
3.1 研究構想………………………………………………………… 38
3.2 研究步驟及方法………………………………………………… 39
第四章 實驗及結果分析………………………………………………… 45
4.1 實驗設備及資料說明…………………………………………… 45
4.1.1 實驗設備………………………………………………………… 45
4.1.2 資料格式………………………………………………………… 45
4.2 實驗過程………………………………………………………… 50
4.3 討論……………………………………………………………… 57
第五章 結論與未來研究………………………………………………… 63
5.1 結論……………………………………………………………… 63
5.2 未來研究方向.…………………………………………………… 64
參考文獻 …………………………………………………………………… 66
附錄一 …………………………………………………………………… 73
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