近年來，顧客關係管理成了相當熱門的研究領域，如何提高顧客的忠誠度，並為高貢獻度的客戶提供更好的服務，不但能降低管銷成本，進而獲得實質利益成長，而想達成這些目標，就必需從了解客戶開始。而如何找出客戶相關的行為模式和相關的知識，就成了重要的關鍵。
本研究提出一個以矩陣分群法為基礎分析顧客行為模式的方法。在原先的矩陣分群法中，藉由在顧客與商品所形成的二元行列矩陣中將行與列不斷置換並萃取出一個代表擁有相似行為模式顧客群的密實部份矩陣（Dense sub-matrix），但是這個方法目前在大規模的行列矩陣上運算效率卻還有改進的空間，此外，搜尋出的顧客群組在行為模式的解釋上也有待改進。而本研究所提的方法是將矩陣分群法跟遺傳演算法結合，希望藉由遺傳演算法大量運算與最適化的特性而能在大規模的稀疏行列矩陣上大幅度的縮短運算時間，並且使得所產生的成果具有較佳的解釋品質，以協助管理人員了解不同群組客戶的需求，制定出更佳的行銷決策，提升行銷活動的效益與有效性。
透過實証研究的結果得知，在經過適合度評估方式的最佳化選擇後，遺傳演算法在矩陣分群法的應用上，能獲得運算時間與分析能力顯著的提升，並以實際客戶資料進行行為模式分析找出三個意義顯著的顧客群組，除了可進行基本的推廌應用外，還可對群組深入分析特性，達成類似行銷上購物籃分析的應用。

In recent year, Customer Relationship Management has become a very popular field of study. How to raise customers’ loyalty and provide better services for high contribution customers can not only lower management and sale cost, but also increase revenue growth. To achieve these goals, it’s important to understand customers. Therefore, how to find out behavior model and knowledge related to customers becomes a key point.
In this research, we have proposed a customer behavior analysis method based on Matrix Clustering. In the original Matrix Clustering, the dense sub-matrix was extracted by replacing rows and columns in the binary matrix formed by customers and products constantly. This part of matrix represents a behavior model of a group of similar customers. But the operation efficiency on a large scale of matrix still can be improved; moreover, part of customers’ behavior model will have problems in explanation.
The method proposed in this research is to combine Matrix Clustering with Genetic Algorithms. It is expected that by the ability of large operation and optimization of Genetic Algorithms, operation time on a large scale of sparse matrix should be shortened dramatically and make the result has higher explanation quality to assist management in understanding the demands of different groups of customers. Therefore, management can make better marketing decisions and improve the efficiency of marketing activities.
According to our experiments, it’s been verified that the application of Genetic Algorithms on Matrix Clustering can get significant improvement on both the calculating time and the ability of analysis after the optimal selection of fitness function. And by applying the method on real world customer data, we had discovered three meaningful behaviors for recommending customers and character analysis of both customers and products.

摘要I
AbstractIII
誌謝IV
目錄V
表目錄VIII
圖目錄IX
第一章　緒論1
第一節　研究背景與動機1
第二節　研究目的2
第三節　研究範圍與限制3
第四節　研究架構3
第五節　研究流程3
第二章　文獻探討6
第一節　顧客關係管理6
2.1.1　顧客關係管理的定義6
2.1.2　顧客關係管理的架構7
2.1.3　顧客關係管理的循環7
2.1.4　顧客關係管理的目的10
第二節　群集分析(Clustering Analysis)11
2.2.1　分群問題描述12
2.2.2　常見群集分析法12
第三節　遺傳演算法15
2.3.1　構成要素16
2.3.2　傳演算法的特性18
2.3.3　遺傳演算法在群集分析的相關研究20
第四節　矩陣分群21
2.4.1　矩陣分群方法概要22
2.4.2　基本演算法：「行列置換法」24
2.4.3　矩陣分群與其它方法比較及相關應用26
第三章　研究方法27
第一節　研究方法架構27
第二節　遺傳演算法的運算模式28
第三節　遺傳演法下運算模式的設定33
3.3.1　染色體的表達模式33
3.3.2　初始解設定33
3.3.3　適合度的計算34
3.3.4　進行挑選與運算用母體形成37
3.3.5　基因運算過程37
3.3.6　世代繁殖停止條件39
3.3.7　多解的搜尋39
第四章　模式驗證與評估40
第一節　顧客模式分析問題介紹40
第二節　實驗設計40
4.2.1　流程說明40
4.2.2　評比項目41
第三節　實驗結果42
4.3.1　遺傳演算法適合度評估模式實驗42
4.3.2　矩陣分群法的演算法比較44
第四節　實例分析與結果47
4.4.1　分析資料之說明47
4.4.2　分析與結果48
第五章　結論與建議52
第一節　結論52
第二節　未來研究方向與建議53
參考文獻55

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