行銷是每個企業最常進行的商業行為，如何行銷才能增加客戶忠誠度，進而挖掘潛在客戶獲取更大利益是相當重要的。為了在有限的資源下，最大化獲得行銷利益，能夠準確的選擇目標客戶對企業來說是相當有用的。因此，建立一套快速，客觀且能增進準確性的目標客戶選擇模型確有其必要性。
利用資料探勘技術找出目標客戶是常見的方式，但過去的研究只著重在尋找高準確率的分類模型。各種不同資料探勘的方法不同情境下有不同應用，沒有哪一種分類器的分類結果永遠最佳。故本研究利用資料探勘技術提出整合性的目標客戶選擇模型，整合支援向量機 (Support Vector Machines, SVM)、類神經網路 (Neural Network, NN)與K-Means演算法 (K-Means Clustering)建構出兩階段分析模型，以期達到提升準確率且同時降低型I誤差與型II誤差的效果。經案例分析後，準確率有較高提升，且型I誤差與型II誤差也皆下降。

Marketing, the prevailing commercial activity of enterprises, is an important strategy to increase customer loyalty and potential customer for more profit. To maximize profit with limited resources, it would be more profitable for enterprises to choose the right target customers. Therefore, it is necessary to build up an efficient, objective and accurate target customer choice model.
Using data mining techniques to find the target customers is a traditional way. However, researches in the past mainly focused on finding the high accuracy classifier, but different classifiers perform differently in varied situations. So this study is to propose an integrated choice of target customer model, integrating support vector machine, neural network and K-Means algorithm into a two-phase analysis model. This model is expected to enhance classification accuracy and reduce Type I and Type II errors at the same time. The research results indicate that the integrated model is effective in simultaneously enhancing classification accuracy and reducing Type I and Type II errors.

Abstract i
摘要 ii
謝誌 iii
目錄 iv
表目錄 vi
圖目錄 vii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 3
1.3 研究流程 4
第二章 文獻探討 6
2.1 資料探勘 6
2.1.1 資料探勘的定義 6
2.1.2 資料探勘的功能 7
2.1.3 資料探勘的流程 9
2.1.4 資料探勘的工具 10
2.2 支援向量機 (SUPPORT VECTOR MACHINE) 11
2.2.1 基本原理 11
2.2.2 核心函數 13
2.3 類神經網路 (NEURAL NETWORK) 14
2.3.1 基本原理 14
2.3.2 倒傳導網路 16
2.4 決策樹C5.0 (DECISION TREE C5.0) 18
2.4.1 基本原理 18
2.4.2 Boosting 20
2.5 K-MEANS演算法 21
2.5.1 基本原理 21

第三章 研究方法 23
3.1 單一型分類模式 25
3.1.1 支援向量機 (Support Vector Machine, SVM) 25
3.1.2 類神經網路 (Neural Network, NN) 27
3.1.3 決策樹C5.0 (Decision tree C5.0) 30
3.2 整合型分類模式 31
第四章 案例分析 34
4.1 單一型分類模式 34
4.1.1支援向量機 (Support Vector Machine) 35
4.1.2 類神經網路 (Neural Network) 37
4.1.3 決策樹C5.0 (Decision tree C5.0) 40
4.1.4 單一型分類模式分析 41
4.2 整合型分類模式 42
4.2.1整合型分類模式分析 47

第五章 結論與建議 49
5.1 研究結論 49
5.2 研究建議 50
5.3 研究限制 50
參考文獻 51

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