臺灣博碩士論文加值系統

　　本研究採用位於彰濱工業區的白蘭氏健康博物館於民國一百年全年度之顧客來訪問卷，利用IBM SPSS Modeler 15.0軟體，首先以分類與迴歸樹 (Classification and Regression Tree, CART)分析建立預測模式，將量表中的7個服務滿意度問項當作投入變數，顧客推薦作為目標變數，分析投入變數與目標變數間的規則，找出最能影響顧客推薦之變數。接著進行維度縮減與特徵選取篩選變數，分別重新投入CART分析。最後，使用貝氏網路 (Bayesian Network)重複以上過程，另外以人工方式篩檢變數，重新投入貝氏網路分析，並將所有CART與貝氏網路模型進行評估。
　　在原始模型之CART分析後發現樹深有5層，且得到6條規則，整理後分析投入變數與目標變數間之規則。維度縮減後模型無法產生樹狀圖與規則；特徵選取之結果則與原始模型之CART結果相同。貝氏網路方面，各模型使用結構類型為Markov Blanket時皆無法分析，僅有結構類型為TAN (Tree Augmented Naive Bayes)時有產生貝氏網路圖。而後我們計算出貝氏網路模型中已知顧客滿意度的情況下，顧客推薦所發生之機率。模型評估方面，貝氏網路各模型之表現較CART各模型好，特徵選取之貝氏網路為表現最好的模型，其次為篩檢變數之貝氏網路模型。最後我們提出各模型的管理意涵。

　　This study used the 2011 internal customers’ questionnaires from Brand’s Health Museum, and used IBM SPSS Modeler 15.0 for analyses by considering seven satisfaction items as input variables and customer recommendation as a target variable. First , we used classification and regression tree (CART) to classify and predict the customers’ behaviors. Second, dimension reduction and feature selection were individually performed to identify the variables, and these variables become the input variables for CART. Third, Bayesian network (BN) was applied to repeat the prior processes. Besides, we subjectively identify the critical variables from BN graph, and these variables become the input variables for BN. Finally, we evaluated the performance among CART and BN models.
In the original CART model, tree depth was five, and six rules were generated. Nevertheless, the CART model with dimension reduction could not generate the tree and any rule, while the CART model with feature selection found all input variables as critical variables. By using the same input variables as the original CART model did, the results of using CART model with feature selection and original CART model were the same. As for BN, only the TAN (Tree Augmented Naive Bayes) structure in all models could generate BN graph. Hence, we computed the probabilities of customer recommendation while consumer satisfaction is given. In model evaluation, the performances of BN models are better than those of CART models, and the best model is BN model with feature selection. In the end, we suggested managerial implications for each model.

目錄

誌謝 I
摘要 II
Abstract III
圖目錄 VII
表目錄 VIII
第一章 緒論 1
第一節 研究背景與動機 1
第二節 研究目的 3
第三節 研究流程 4
第二章 文獻回顧 5
第一節 產業觀光 5
第二節 觀光工廠 8
第三節 口碑傳播 10
一、口碑傳播定義 10
二、口碑傳播與推薦他人之關係 12
第四節 資料探勘 13
一、資料探勘 13
二、分類與迴歸樹 16
三、貝氏網路 17
四、維度縮減與特徵選取 19
第三章　研究方法 21
第一節 研究工具與步驟 21
第二節 資料來源 21
第三節 CART模型分析 25
一、CART預測模型建置 25
二、維度縮減之CART 26
三、特徵選取之CART 27
第四節 貝氏網路模型分析 28
一、貝氏網路模型建置 28
二、篩檢變數之貝氏網路 28
三、維度縮減之貝氏網路 28
四、特徵選取之貝氏網路 29
第五節 模型評估 29
第四章　研究分析 30
第一節 CART結果 30
一、原始模型之CART結果 30
二、維度縮減之CART結果 34
三、特徵選取之CART結果 35
第二節 貝氏網路結果 35
一、原始模型之貝氏網路結果 35
二、篩檢變數之貝氏網路結果 36
三、維度縮減之貝氏網路結果 39
四、特徵選取之貝氏網路結果 42
第三節 各模型比較與模型評估 42
第五章　結論 46
第一節 CART分析之結論與建議 46
第二節 貝氏網路分析之結論與建議 47
參考文獻 50
一、中文文獻 50
二、英文文獻 51
三、網路資源 58


圖目錄
圖1.1 研究流程 4
圖2.1 知識發現流程 14
圖2.2 DAG圖形 18
圖4.1 原始模型之CART決策樹圖 31
圖4.3 維度縮減之CART決策樹圖 34
圖4.4 原始模型之貝氏網路圖 (Markov Blanket) 35
圖4.5 原始模型之貝氏網路圖 (TAN) 35
圖4.6 篩檢變數之貝氏網路圖 36
圖4.7 維度縮減之貝氏網路圖 40
圖4.8 模型評估 44


表目錄
表2.1 產業觀光相關定義 6
表2.2 觀光工廠相關定義 9
表2.3 口碑傳播相關定義 10
表2.4 資料探勘功能與方法 14
表3.1 白蘭氏健康博物館問卷變數代碼對照表 22
表3.2 人口統計變數代碼對照表 23
表3.3 人口變數統計表 24
表3.4 訓練-測試正確率 25
表3.5 主成份分析解說總變異量 26
表3.6 主成份分析轉軸後的成份矩陣 27
表4.1 原始模型之CART預測重要變數表 30
表4.2 原始模型之CART規則整理 32
表4.3 原始模型之CART預測正確率 (一) 33
表4.4 原始模型之CART預測正確率 (二) 33
表4.5 原始模型之CART效能評估 33
表4.6 維度縮減之CART 預測正確率 34
表4.7 篩檢變數之貝氏網路變數變數權重表 37
表4.8 篩檢變數之貝氏網路變數H條件機率 37
表4.9 篩檢變數之貝氏網路變數D對於變數H之條件機率 37
表4.10 篩檢變數之貝氏網路變數E對於變數D及變數H之條件機率 37
表4.11 篩檢變數之貝氏網路變數H對於變數D及變數E之條件機率 39
表4.12 維度縮減之貝氏網路變數H條件機率 40
表4.13 維度縮減之貝氏網路變數A對於變數H之條件機率 40
表4.14 維度縮減之貝氏網路變數H對於變數A之條件機率 41
表4.15 各模型結果整理 (CART) 42
表4.16 各模型結果比較 (貝氏網路) 43

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三、網路資源

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經濟部工業局 (2008a)，觀光工廠輔導做法大躍進—評鑑機制啟動， 2012年10月16日，http://www.moea.gov.tw/Mns/populace/news/News.aspx?kind=1&;menu_id=40&;news_id=13172。
經濟部工業局 (2008b)，觀光工廠輔導做法大躍進—評鑑機制啟動， 2012年10月16日， http://www.moea.gov.tw/Mns/populace/news/News.aspx?kind=1&;menu_id=40&;news_id=12751。
經濟部工業局 (2013a)，102年觀光工廠輔導啟動計畫說明會台中開場， 2013年4月28日，http://www.moea.gov.tw/Mns/populace/news/News.aspx?kind=1&;menu_id=40&;news_id=29860。
觀光工廠自在遊 (2012)，101年度優良觀光工廠入選名單與頒獎典禮活動訊息公佈，2013年4月28日，http://www.taiwanplace21.org/news/20121017.html。