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記錄編號3320
狀態NC088FJU00392005
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學校名稱輔仁大學
系所名稱資訊工程學系
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學號487516058
研究生(中)何仁傑
研究生(英)Ren-Jie Ho
論文名稱(中)從大型資料庫中挖掘感興趣的型樣
論文名稱(英)Mining Interested Information from Large Database
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指導教授(中)顏秀珍
指導教授(英)Show-Jane Yen
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學位類別碩士
畢業學年度88
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關鍵字(中)資料探勘 序列型樣 相關規則 查詢
關鍵字(英)data mining sequential patterns association rules query
摘要(中)挖掘(Mining)相關規則(Association Rules)是找出大部分顧客,在每一筆交易中所採購之物品的相關性,挖掘序列型樣(Sequential Patterns)則是找出大部分顧客在其不同交易中採購物品的順序行為,兩者的目的都是從顧客的交易資料中,找出有用的資訊。由於顧客的交易不斷在進行,交易資料會不斷的增加,為了提供使用者最新的資訊,系統必須週期性地找出所有的相關規則與序列型樣,再者,使用者可能僅對某部分的資訊有興趣。然而,若每次都要重新找出所有這類的資訊,需花費相當多的時間。為了依照使用者的需求來找出最新的資訊。我們定義了一套資料探勘語言(Data Mining Language),使用者可以利用這套語言自行定義條件及需求,而系統可以針對這些條件及需求,快速的獲得使用者感興趣的資訊。
摘要(英)The destination of mining association rules is to discover the associative purchasing behaviors from each transaction of most customers. Mining sequential patterns is to discover the sequential purchasing behaviors from a large amount of the transactions of customers. Both of these are to discover the useful information. Because the transactions are increased and updated frequently, the system must discover all of the association rules and sequential patterns in a period of time. Besides, browsing through all of the information is not efficient if the users are only interested in part of the information. For these reasons, we design a data mining language for the users to define what they are interested. Then the system can look for the interested information rapidly according to the definition defined by the users.
論文目次摘要……………………………………………………………………i 內容……………………………………………………………………ii 圖表目錄………………………………………………………………iv 1. 導論 1 1.1 問題說明 1 1.2 相關工作 4 2. 資料探勘語言 5 3. 挖掘感興趣之相關規則 8 3.1 資料庫型態的轉換 8 3.2 查詢相關規則:明確指定前項與後項 9 3.3 查詢相關規則:可能指定前項或後項包含的項目 11 4. 挖掘感興趣之序列型樣 20 4.1 挖掘1-頻繁序列 20 4.2 查詢序列型樣:明確指定所有項目集之項目 24 4.3查詢序列型樣:查詢序列中指定前N項或後N項項目集中的項目,或查詢序列中的項目集皆未指定(N³1) 26 4.4 查詢序列型樣:第三類查詢 34 5. 實驗結果 42 5.1 產生資料庫 42 5.2 實驗數據與討論 43 6. 結論與未來工作 50 參考資料 52 圖表目錄 表一 交易序列資料庫(TSD) 8 表二 交易位元資料庫(TBD) 9 表三.1-項目集資料庫 15 表四.2-項目集資料庫 16 表五. 2-項目集資料庫 18 表六. 3-項目集資料庫 19 表七 交易項目集其購買人數 21 表八 2-項目集資料庫 22 表九 2-候選項目集與其購買人數 22 表十 頻繁項目集資料庫 23 表十一 1-序列資料庫 24 表十二 1-項目集資料庫 30 表十三 1-序列資料庫 30 表十四 2-序列資料庫 31 表十五 1-序列資料庫 32 表十六 2-序列之資料庫 33 表十七 3-序列資料庫 33 表十八 1-項目集資料庫 39 表十九 1-序列資料庫 39 表二十 2-序列資料庫 40 表二十一 參數表 42 表二十二.資料庫之參數設定值 46 表二十三 C5-T10-I10-R10所產生的候選序列個數|CK|與頻繁序列個數|LK| 47 表二十四 C10-T10-I10-R10所產生的候選序列個數|CK|與頻繁序列個數|LK| 47 表二十五 C20-T10-I10-R10所產生的候選序列個數|CK|與頻繁序列個數|LK| 47 表二十六 C10-T10-I10-R20所產生的候選序列個數|CK|與頻繁序列個數|LK| 48 表二十七 C10-T10-I10-R40所產生的候選序列個數|CK|與頻繁序列個數|LK| 48 圖一 挖掘所有頻繁項目集─Apriori演算法與我們演算法之比值 44 圖二 挖掘所有頻繁序列─AprioriAll演算法與我們演算法 48 圖三 挖掘所有頻繁序列─AprioriAll演算法與我們演算法之比值 49
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