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研究生:曾嘉楹
研究生(外文):Chia-Ying Tseng
論文名稱:應用屬性加權式分群法於推薦系統之研究
論文名稱(外文):A Study of Applying Feature-Weighting Clustering to Recommender Systems
指導教授:劉敦仁劉敦仁引用關係
指導教授(外文):Duen-Ren Liu
學位類別:碩士
校院名稱:國立交通大學
系所名稱:資訊管理所
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:2003
畢業學年度:91
語文別:中文
論文頁數:71
中文關鍵詞:推薦系統
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在電子商務的市場行銷,決策者會根據不同的策略主題(Strategy Subjects),例如市場佔有率、最大利潤等等不同的主題來訂定行銷的策略。因此以資料探勘之分群技術進行市場行銷分析時,須考量產品類別、價位及消費者的基本資料等屬性,對於不同行銷策略的權重影響。本研究提出加權式分群法,考量屬性之權重分佈以進行不同策略主題之分析。本文以一電子商務的實例資料集,針對商品的市場佔有率、商品獲利率以及商品推薦為策略主題,進行實驗以驗證加權式分群法之價值
In managing marketing activities, enterprises usually make marketing decisions according to different strategy subjects such as market holding ration or maximum profit. The weightings of features, including product category, unit price and customer data, may vary for different strategy subjects. Accordingly, applying data mining technique such as clustering to market analysis needs to consider the weightings of features. This work proposed a feature-weighting clustering approach to analyze various marketing strategy subjects via considering the weightings of features. Experimental evaluations were conducted to evaluate the effect of the proposed approach on three strategy subjects including market holding ratio, sale revenue and product recommendations.
中文提要 ......................... i
英文提要 ......................... ii
誌謝 ......................... iii
目錄 ......................... iV
表目錄 ......................... Vii
圖目錄 ......................... viii
一、 緒論........................ 1
1.1 研究背景...................... 1
1.2 研究動機...................... 3
1.3 問題描述與研究目標.................. 4
1.3.1 異質型屬性..................... 4
1.3.2 資料分佈的趨勢................... 4
1.3.3 權重配置...................... 5
1.3.4 知識的呈現...................... 5
1.4 研究範圍...................... 6
1.5 研究步驟...................... 7
1.6 論文架構...................... 8
二、 文獻探討...................... 9
2.1 資料探勘技術.................... 9
2.1.1 決策樹與加權分類法................. 9
2.1.2 一般分群技術.................... 11
2.1.3 多維度分群..................... 12
2.1.4 人工智慧與類神經網絡................ 12
2.2 屬性加權...................... 13
2.2.1 異質型屬性..................... 13
2.2.2 多變量分析..................... 13
2.3 電子商務與推薦技術................. 13
2.3.1 平衡計分卡(Balanced Score Card, BSC) ........ 13
2.3.2 客戶關係管理(Customer Relationship Management, CRM) 14
2.3.3 推薦技術(Recommendation Systems) ......... 15
三、 屬性加權的分群技術................. 16
3.1 基本假設...................... 16
3.2 與分類技術的比較.................. 18
3.2.1 目標導向...................... 18
3.2.2 資料分佈的劃分................... 19
3.2.3 多目標決策..................... 20
3.3 異質屬性...................... 20
3.4 加權機制...................... 22
3.4.1 正規化(Normalization) ............... 22
3.4.2 資訊獲得值(IG) .................. 22
3.4.3 倒傳遞類神經網絡的貢獻度分析(CBP) ......... 23
3.4.4 倒傳遞類神經網絡的鍊結權重(WBP) .......... 24
3.4.5 適應共振理論的鍊結權重(ART) ............ 25
3.4.6 基因演算法的染色體組合................ 25
3.4.7 多變量分析的變量權重................ 25
3.4.8 非線型研究的屬性權重(NP) ............. 26
3.5 屬性萃取...................... 26
3.6 相似度計算..................... 27
3.6.1 皮爾森法...................... 27
3.6.2 向量法....................... 28
3.6.3 距離法....................... 28
3.7 分群法應用..................... 30
四、 實驗設計...................... 31
4.1 研究架構...................... 32
4.2 資料來源...................... 34
4.3 實驗步驟設計.................... 36
4.3.1 欄位初步選取.................... 36
4.3.2 分群法選取..................... 36
4.3.3 屬性加權方法的使用................. 37
4.3.4 相似度計算..................... 39
4.3.5 推薦及預測機制................... 39
五、 實驗過程與結果分析................. 40
5.1 實驗系統架構及說明................. 40
5.2 資料前置處理.................... 41
5.2.1 資料淨化(Data Cleaning) .............. 41
5.2.2 屬性選取(Feature Selecting) ............ 42
5.2.3 資料統整(Data Integration) ............ 42
5.2.4 資料轉換(Data Transformation) ........... 43
5.2.5 資料離散化(Data Discretization) .......... 43
5.3 分群機制...................... 43
5.3.1 正規化(Normalization) ............... 43
5.3.2 資訊獲得值(IG) ................... 44
5.3.3 倒傳遞類神經網絡的隱藏層節點貢獻度(CBP) ...... 45
5.3.4 倒傳遞類神經網路鍊結權重(WBP) .......... 47
5.4 綜合推薦機制 48
5.5 測試模組...................... 49
5.5.1 測試指標...................... 49
5.5.2 測試步驟...................... 50
5.6 知識呈現模組.................... 51
5.7 實驗一之結果分析.................. 52
5.8 實驗二之結果分析.................. 54
5.9 實驗三之結果分析 56
5.10 綜合討論...................... 57
六、 結論與建議..................... 59
6.1 研究結論...................... 59
6.2 未來研究之建議................... 61
參考文獻...................... 63
附件一 Data Mining運用的理論與實際應用功能........ 67
附件二 資料字典...................... 69
附件三 資料前置處理篩選規則................ 71
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