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研究生:凌俊青
研究生(外文):Chun-Ching Ling
論文名稱:在包裹資料庫中挖掘數量關聯規則
論文名稱(外文):Mining Quantitative Association Rules in Bag Databases
指導教授:陳彥良陳彥良引用關係許秉瑜許秉瑜引用關係
指導教授(外文):Yen-Liang ChenPing-Yu Hsu
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理研究所
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:1999
畢業學年度:87
語文別:中文
論文頁數:66
中文關鍵詞:資料挖掘關聯規則模糊集合
外文關鍵詞:data miningassociation Rulefuzzy set
相關次數:
  • 被引用被引用:7
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  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:0
所謂挖掘關聯規則,是要從企業銷售交易資料庫中,找出項目之間的關聯性。過去大部份研究所找出的關聯規則通常只能表達項目間有否相關,卻無法表達它們在不同購買數量時的相關性。如此所產生的問題是,我們將無法知道該以什麼的比例來搭配不同產品一齊販售。因此若關聯規則能加入項目數量資訊的話,將非常有益於制訂行銷策略。本文所提出的演算法,可以找尋出包含項目數量的關聯規則。接著利用指定項目數量的區間及模糊集合原理,找出具有語意的關聯規則。
The problem of mining association rules is to find the associations between items in a large database of sales transactions. Although there are a lot of previous researches on this area, a common problem occurred is that the rule only indicates if two items are related but as to in what quantities and in what combinations are missing. Without this information it is impossible to design a competitive combination of sales items since we didn''t know how many units of items should be included. Therefore, if the quantities of items can be included in association rules, it will be helpful for managers to make the marketing decisions. In this paper, we introduce a new algorithm for mining association rules including the quantities of items. Then, we extend the rules so that the quantities of items can be expressed as user-defined intervals or fuzzy terms.
目錄I
圖表目錄III
第一章 緒論1
第一節 研究動機1
第二節 研究目的2
第三節 研究範圍2
第四節 論文結構3
第二章 文獻探討4
第一節 資料挖掘4
一.挖掘關聯規則 4
二.多層級資料的歸納5
三.資料分類7
四.群集分析7
五.在網路上的資料挖掘8
第二節 關聯規則11
一.Apriori演算法11
二.挖掘多概念層級的關聯規則16
三.增進挖掘關聯規則的效率20
四.挖掘數量關聯規則22
五.關聯規則的有趣性27
六.挖掘關聯規則的更新29
七.挖掘關聯規則的平行之處理30
八.調整精確度地挖掘關聯規則31
第三章 挖掘簡單關聯規則32
第一節 數量關聯規則定義32
第二節 簡單關聯規則34
第四章 挖掘一般關聯規則39
第五章 挖掘語意關聯規則45
第一節 使用者決定項目區間45
第二節 利用模糊理論來決定項目區間48
第六章 實驗模擬52
第一節 實驗設計52
第二節 結果分析54
第三節 實驗結論56
第七章 結論與建議57
參考文獻58
圖表目錄
圖表 1 資料方塊之範例6
圖表 2 使用者瀏覽模式之範例9
圖表 3 MF演算法之範例9
圖表 4 Apriori演算法13
圖表 5 apriori-gen函式13
圖表 6 交易資料庫之範例14
圖表 7 候選項目集合和大項目集合的產生14
圖表 8 概念階層樹範例17
圖表 9 轉換後的編碼交易資料表T[1] ………………………………18
圖表 10 過濾後的編碼交易資料表T [2]18
圖表 11 各回合產生的候選項目集合及大項目集合19
圖表 12 AprioriTid之範例21
圖表 13 挖掘數量關聯規則之範例24
圖表 14 資料庫範例25
圖表 15 包含隸屬度的資料庫27
圖表 16 MQA-1演算法36
圖表 17 candidate-generate函式36
圖表 18 MQA-1範例38
圖表 19 MQA-M演算法41
圖表 20 support函式42
圖表 21 candidate-generate函式42
圖表 22 MQA-M範例44
圖表 23 MQA-I範例46
圖表 24 MQA-F範例50
圖表 25 各回合的候選項目集合及大項目集合51
圖表 26 各演算法在不同交易筆數下的執行時間之比較54
圖表 27 各演算法在不同最小支持度下下的執行時間之比較61
圖表 28 各演算法在不同最小支持度下產生大項目集合數目
之比較55
圖表 29 MQA-1演算法在不同交易筆數下產生各回合大項目
集合所需時間之比較56
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