臺灣博碩士論文加值系統

本研究採用陳威廷(2011)針對全聯福利中心所建立之服務品質量表，利用IBM SPSS Modeler 14.2的分類與迴歸樹 (Classification and Regression Tree, CART)進行分類與預測，將量表中的28個問項當作投入變數，並將6類消費金額合併為3類作為目標變數，試圖以滿意度來預測消費金額，分析投入變數與目標變數間的規則，並找出最能影響消費金額的變數。而後利用SPSS 18.0進行維度縮減篩選出特徵值超過1的變數，重新投入CART並與前模型進行比較。最後，使用貝氏網路 (Bayesian Network)並重複以上過程，並將CART模型與貝氏網路模型進行模型評估。
在原始CART模型中樹深有13層，並得到33條本次消費金額類別的預測規則，合併消費金額類別後之樹深為12層，預測規則有32條，二模型結果都較為複雜；維度縮減和特徵選擇後模型精簡許多，樹深和規則各為9層、1層、14條和2條。貝氏網路方面，各模型之Markov Blanket結構皆無法分析，僅有維度縮減和特徵選擇之TAN (Tree Augmented Naïve Bayes)模型有產生貝氏網路圖。而後我們計算出二模型中，已知消費者滿意度的情況下，各類消費金額類別所發生之機率。在模型評估方面，IBM SPSS Modeler 14.2評估之優劣名次為：目標變數類別合併之CART、維度縮減之貝氏網路、原始模型之CART、特徵選擇之貝氏網路、維度縮減之CART與特徵選擇之CART模型。最後我們提出各模型的管理意涵。

This study used the service quality scale for Pxmart established by Chen (2011) and used classification and regression tree (CART) of IBM SPSS Modeler 14.2 to classify and predict the customers’ behaviors. Twenty eight items identified by Chen (2011) are input variables, while the three categories of amount spent per visit is the target variable. This study intends to predict the amount spent per visit and analyze the rules between input and target variables. Later, dimension reduction was performed by SPSS 18.0 to identify the variables with eigenvalue greater than one, and these variables become the input variables for CART. Finally, Bayesian network (BN) was applied to repeat the prior processes, and an evaluation among CART and BN models was performed.
In the original CART model, the tree depth was 13, and 33 rules of categories of amount spent per visit were generated. After merging categories of amount spent per visit, the tree depth was 12, 32 rules were obtained, and both results of models are too complex. The tree depth and rules are much simpler after dimension reduction and feature selection with the respective tree depths and rules are 9, 1, 14, and 2. For Bayesian network, the Markov blanket structure in all models could not be analyzed, only TAN (Tree Augmented Naïve Bayes) structure in dimension reduction and feature selection models generate Bayesian network graph. Later, we computed the probabilities of categories of amount spent per visit while consumer satisfaction is given. In model evaluation, the rankings performed by IBM SPSS Modeler 14.2 are: CART model with merged target variable categories, BN model with dimension reduction, original CART model, BN model with feature selection, CART model with dimension reduction, and CART model with feature selection. Finally, we suggested managerial implications for each model.

目錄
頁次
誌謝 I
摘要 II
ABSTRACT IV
圖目錄 IX
表目錄 XI
第一章 緒論 1
第一節 研究動機與研究背景 1
第二節 研究目的 3
第三節 研究流程 3
第二章 文獻探討 6
第一節 服務品質 6
一、服務的定義與特性 6
二、服務品質定義 8
三、服務品質衡量 10
第二節 資料探勘與決策樹 19
一、資料探勘 19
二、決策樹 22
第三節 分類與迴歸樹 23
第四節 維度縮減與特徵選擇 25
第五節 貝氏網路 26
第三章 研究方法 30
第一節 研究工具與步驟 30
第二節 資料來源 30
第三節 CART模型分析 33
一、CART預測模型建置 33
二、目標變數類別合併 35
三、維度縮減 36
四、特徵選擇 39
第四節 貝氏網路 40
第五節 模型評估 41
第四章 研究結果 43
第一節 CART結果 43
一、原始模型之CART結果 43
二、目標變數類別合併之CART結果 51
三、維度縮減之CART結果 60
四、特徵選擇之CART結果 66
第二節 貝氏網路結果 69
一、目標變數類別合併之貝氏網路結果 69
二、維度縮減之貝氏網路結果 70
三、特徵選擇之貝氏網路結果 82
第三節 各模型比較 88
第四節 模型評估 89
第五章 結論 92
第一節 CART管理意涵 92
第二節 貝氏網路管理意涵 94
參考文獻 96
 
圖目錄
圖1.1 研究流程圖 5
圖2.1 認知服務品質決定因素 13
圖2.2 服務品質模式 14
圖2.3 KDD的步驟 21
圖2.4 DAG的例子 27
圖3.1 陡坡圖 39
圖4.1 原始模型之樹狀圖 44
圖4.2 原始模型之重要變數圖 45
圖4.3 目標變數類別合併之樹狀圖 53
圖4.4 目標變數類別合併之重要變數圖 54
圖4.5 維度縮減之樹狀圖 61
圖4.6 維度縮減之重要變數圖 62
圖4.7 特徵選擇之樹狀圖 67
圖4.8 目標變數類別合併之TAN貝氏網路圖 69
圖4.9 目標變數類別合併之Markov Blanket貝氏網路圖 70
圖4.10 維度縮減之TAN貝氏網路圖 71
圖4.11 維度縮減之TAN重要變數圖 72
圖4.12 特徵選擇之TAN貝氏網路圖 82
圖4.13 特徵選擇之TAN重要變數圖 83
圖4.14 模型評估圖 90
 
表目錄
表2.1 財貨與服務的差異 8
表2.2 服務品質相關研究 9
表2.3 SERVQUAL構面 15
表2.4 RSQS構面 17
表2.5 Stanworth (2009)量表構面 19
表2.6 資料探勘功能與方法 22
表2.7 節點A (消費金額)之條件機率表 28
表2.8 節點B (性別)之條件機率表 28
表3.1 陳威廷 (2011)之全聯服務品質調查問項 31
表3.2 訓練-測試正確率 34
表3.3 3類目標類別訓練-測試正確率 35
表3.4 解說總變異量 37
表3.5 轉軸後的成份矩陣 38
表4.1 原始模型之變數權重 46
表4.2 原始模型之規則整理 47
表4.3 原始模型之訓練與測試資料正確率 50
表4.4 原始模型之實際與預測消費金額分析 50
表4.5 目標變數類別合併之變數權重 55
表4.6 目標變數類別合併之規則整理 56
表4.7 目標變數類別合併之訓練與測試資料正確率 58
表4.8 目標變數類別合併之實際與預測消費金額分析 59
表4.9 維度縮減之變數權重 63
表4.10 維度縮減之規則整理 63
表4.11 維度縮減之訓練與測試資料正確率 65
表4.12 維度縮減之實際與預測消費金額分析 65
表4.13 特徵選擇之訓練與測試資料正確率 68
表4.14 特徵選擇之實際與預測消費金額分析 68
表4.15 目標變數C7的條件機率 72
表4.16 變數2 (B2)的條件機率 73
表4.17 變數3 (B3)的條件機率 73
表4.18 變數9 (B9)的條件機率 74
表4.19 變數8 (B8)的條件機率 74
表4.20 C7 (本次消費金額)對於變數3的條件機率 77
表4.21 C7 (本次消費金額)對於變數9的條件機率 77
表4.22 C7 (本次消費金額)對於變數8的條件機率 77
表4.23 C7 (本次消費金額)對於變數3及變數9的條件機率 79
表4.24 C7 (本次消費金額)對於變數9及變數8的條件機率 80
表4.25 變數22 (B22)的條件機率 83
表4.26 變數23 (B23)的條件機率 84
表4.27 C7 (本次消費金額)對於變數22的條件機率 85
表4.28 C7 (本次消費金額)對於變數23的條件機率 85
表4.29 C7 (本次消費金額)對於變數22及變數23的條件機率 87
表4.30 各模型整理 89

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