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研究生:劉永嘉
研究生(外文):Liu, Yung Chia
論文名稱:關聯規則資料倉儲化的研究
論文名稱(外文):A Study on Data Warehousing Association Rules
指導教授:陳春賢陳春賢引用關係
指導教授(外文):Chen, Chun Hsien
學位類別:碩士
校院名稱:長庚大學
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:2005
畢業學年度:93
語文別:中文
論文頁數:74
中文關鍵詞:關聯規則探勘資料倉儲線上分析處理趨勢分析主觀的有趣量度指標
外文關鍵詞:Association Rule MiningData WarehouseOn-Line Analysis ProcessTrend AnalysisSubjective Measures of Interestingness
相關次數:
  • 被引用被引用:1
  • 點閱點閱:158
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:4
隨著資料探勘(Data Mining)技術的進步,許多零售量販業者都紛紛利用資料探勘技術來分析交易資料以制定相關的行銷策略。而為了儲存這些交易資料以供分析,許多企業紛紛導入資料倉儲(Data Warehouse),將這些資料依時間、主題整合。關聯規則分析(Association Rule Mining)是一項熱門的資料探勘技術,最常用於尋找產品與產品間銷售的關聯性,藉此,業者可以將銷售相關性高的商品做交叉銷售(Cross Selling)、促銷活動、陳列位置調整。以往這樣的分析是根據使用者自訂的門檻值對交易資料進行探勘,找出關聯程度高的產品。然而,這些產生的關聯規則卻不常考慮時間性、地區性的趨勢資訊。因此,本論文提供一套架構來結合關聯規則分析及資料倉儲,將不同時間、不同地區的關聯規則分別整合至資料倉儲,進而可以根據時間、地點等條件作關聯規則的趨勢分析(Trend Analysis),包括: 時間趨勢型譜(Temporal Trend Profile)、地區趨勢型譜(Regional Trend Profile)、產品趨勢型譜(Product Trend Profile)等分析,並結合OLAP(On-Line Analysys Process)功能讓使用者做多維度的分析。此外,本研究利用關聯規則的主觀有趣量度分析找出使用者感興趣及意外的關聯規則,以節省決策分析者篩選大量關聯規則的時間。
In recent years, many retailers and wholesalers start using data mining techniques to analyze sales data and use the analysis result to develop their marketing strategies. In order to store all the transaction and sales information for future analysis, data warehouse is extensively used to integrate the data which contain information of temporal and regional trends. Association rule mining is one of the main techniques in data mining; it is usually used to discover the sale relationship among products; therefore, retailers can use the discovered rules called association rules for the design of sales strategies. In the mining process, the user set threshold(e.g., support、confidence) for mining association rules that satisfy the threshold in whole data set, but the information about temporal and regional trends hidden in association rules are usually neglected. In this thesis, we propose a framework to facilitate OLAP(On-Line Analysis Process) analysis of association rules discovered from the process of association rule mining. Base on our framework, the user can use OLAP technique to observe temporal trend、regional trend、product trend of association rules by constructing strength profiles of association rules proposed by this thesis. Due to the large number of association rules that could be stored in a data warehouse, it can be quite difficult for the user to locate association rules of interest. Therefore, we propose using subjective interestingness of particular association rules to discover interesting association rules that conform user’s existing knowledge.
第一章 緒論 1
1-1 研究背景 1
1-2 研究動機 2
1-3 研究目的 4
第二章 文獻探討 6
2-1 資料探勘簡介 6
2-2 資料倉儲簡介 7
2-3 OLAP分析簡介 11
2-4 關聯規則探勘簡介 12
2-5 關聯規則的主觀有趣量度指標 16
第三章 研究方法 24
3-1 關聯規則資料倉儲化簡介 24
3-2 研究假設 25
3-3 研究方法流程 25
3-4 資料倉儲的資料模式架構 26
3-5 關聯規則的資料倉儲化 27
3-6 關聯規則OLAP分析的強度累總值計算 30
3-7 研究限制 35
3-8 關聯規則的OLAP分析方法 36
3-9 關聯規則的時間趨勢型譜分析 37
3-10 關聯規則的地區趨勢型譜分析 39
3-11 關聯規則的產品趨勢型譜分析 40
3-12 關聯規則的主觀有趣量度分析 41
第四章 模擬實驗 43
4-1 實驗工具介紹 43
4-2 模擬資料簡介 43
4-3 關聯規則探勘 45
4-4 關聯規則的趨勢型譜分析 46
4-5 關聯規則的主觀有趣量度分析 54
第五章 結論與未來發展 57
5-1 結論 57
5-2 未來發展 58
參考文獻 59
附錄一 模擬資料機率分佈 63
英文部份
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