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研究生:涂靜儀
研究生(外文):Ching-Yi Tu
論文名稱:結合自組織映射圖類神經網路與基因演算法建構壽險業顧客關係管理之知識採擷模式
論文名稱(外文):Knowledge Mining Pattern on Life Insurance CRM by Self Organization Maps and Genetic Algorithm
指導教授:林兆欣林兆欣引用關係
指導教授(外文):Chao-Hsin Lin
學位類別:碩士
校院名稱:國立高雄第一科技大學
系所名稱:風險管理與保險所
學門:商業及管理學門
學類:風險管理學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:89
中文關鍵詞:顧客關係管理基因演算法自組織映射圖類神經熵理論屬性挑選
外文關鍵詞:Genetic AlgorithmCustomer Relationship ManagementCRMArtificial neural networkEntropy ModelSelf-Organizing MapFeature Selection.
相關次數:
  • 被引用被引用:18
  • 點閱點閱:413
  • 評分評分:
  • 下載下載:0
  • 收藏至我的研究室書目清單書目收藏:9
摘要
近年來企業對於顧客關係管理議題之重視,使得顧客成為企業進行績效評估之一項重要指標,因顧客為利潤創造之直接來源,其顧客滿意度更是操控了企業績效之盛衰。本文欲運用資料採擷之技術,協助企業於訂定決策時,支援其所需之資訊,而顧客關係管理中最重要之目的,乃為提高顧客滿意度並期能創造企業利潤。
一般而言進行資料採擷時,通常使用集群方法進行資料分析,然而集群方法之種類有多變量統計方法、類神經網路和基因演算法等工具。本文運用人工智慧之類神經網路與基因演算法進行知識擷取,先以自組織映射圖將資料中群集加以區分,亦即進行風險分類或顧客特徵分類,再利用基因演算法擷取每一群集之特徵,期能得知資料潛藏之意義,了解保戶之風險特徵,以輔助企業訂定行銷策略,區分目標市場,進行目標市場行銷。
本文以人工智慧之自組織特徵映射圖類神經學習網路為工具,運用其向量量化與拓撲保存之特性,可將類似之特徵向量加以聚集,並搜尋出最具代表性之群聚中心,將其應用於風險分類與壽險業之顧客關係管理。
另本文於建構SOM模型之前,運用熵理論進行屬性挑選,將資料中較具代表性之屬性挑選出來,再透過SOM網路進行學習,將本文所蒐集之壽險公司業務員與其招攬之保單資料,依其特性將之適當分群,期能得知具有何種特性之業務員,所招攬之保單型態為何,或是具有某特徵之保戶皆投保何種保單等潛在訊息。如此即可得知每一群集之特性,達到保險同質性之原則,再加以進行市場區隔。保險公司即可對每一群集之特性加以分析與了解,依其屬性訂定專屬之行銷策略、保險商品、或培訓相關策略之專業人才。
ABSTRACT
Recently, the subject of Customer Relationship Management is quickly rising. Customer has become an important index for building an enterprise since customer is the source for making direct profit. In addition, the satisfaction of customer seriously influences the enterprise’s future development. Thus, it is critical to use the techniques of data mining in order to help the enterprise get the valuable information for decision support, increase the satisfaction of customer and the profit of enterprise.
Clustering analysis is the common way to analyze the data when making data mining. It can be divided into three methods, Multivariate Analysis, Artificial Neural Analysis, it is the most used method. This study using artificial neural network and genetic algorithm for knowledge mining.
This study use a well-known feature selection algorithm SUD based on entropy theorem. The main purpose is to rank attributes. Using this information can reduce convergence time with smaller number of attributes and desired error rates.
This study propose a methodology pattern that utilizes the Self-Organization Map of artificial neural network to cluster the data of sales and customers’ polices, base on the techniques of knowledge mining. This study tries to find the features and the characteristics of the clusters by the analysis of each cluster and to discover the potentially useful information, by the construction of a customer relationship management system. Through this system, life insurance companies can adjustment their market segregation, human resource and marketing strategies.
目錄
摘要I
ABSTRACTII
誌謝III
目錄IV
表目錄VIII
圖目錄IX
壹、 緒論1
一、研究動機2
二、研究目的3
三、研究方法與步驟4
(一)相關資料搜集整理4
(二)研究設計4
(三)實證分析模型建置與介面5
(四)結論與建議5
四、研究架構6
貳、 文獻探討8
一、屬性挑選8
(一)屬性挑選之定義8
(二)屬性挑選之技術8
二、資料採擷10
(一)資料採擷的源起10
(二)資料採擷的定義10
(三)資料庫知識發現流程11
(四)資料採擷的方式12
(五)資料採擷方法與統計方法13
(六)資料採擷的模式14
(七)資料採擷的技術18
(八)資料採擷的架構19
三、自組織映射圖網路(SELF ORGANIZATION MAPS;SOM)20
(一)關於自組織映射圖20
(二)SOM之優缺點:22
四、基因演算法(GENETIC ALGORITHM)23
(一)關於基因演算法23
(二)基因演算法之優點23
(三)遺傳演算法之基本架構24
五、顧客關係管理(CUSTOMER RELATIONSHIP MANAGEMENT)25
(一)顧客關係管理之定義25
(二)顧客關係管理之流程26
(三)顧客關係管理之特色26
參、 研究設計28
一、運用熵理論進行屬性挑選-SOM網路學習之前置作業29
二、以自組織映射圖(SOM)為基礎進行資料的知識採擷30
(一)以SOM網路之架構進行資料分群31
(二)SOM演算法則32
(三)SOM之參數設定34
(四)SOM所得之結果37
(五)擷取知識37
(六)分析方法_視覺化分析38
三、以基因演算法(GENETIC ALGORITHM)進行知識抽取46
(一)基因演算法之架構與流程46
(二)基因演算法之基本運算原理48
肆、 實證分析52
一、資料處理與說明52
(一)資料處理52
(二)資料說明54
二、未進行屬性挑選之資料其所得之SOM網路學習結果56
(一)建構SOM模型以進行群集化57
(二)運用視覺化之網路拓撲分析學習結果60
三、基植於熵理論挑選屬性後,所得之SOM網路學習結果62
(一)基植於熵理論之屬性挑選62
(二)屬性挑選之結果63
(三)建構SOM模型進行網路學習64
(四)運用視覺化分析學習結果67
四、運用基因演算法採擷群集之特徵75
(一)規則編碼76
(二)染色體77
(三)適應函數77
(四)基因演算因子(選擇、複製、交配、突變)78
伍、 結論80
一、結論80
(一)基植於熵理論之屬性挑選80
(二)實務應用81
二、未來發展方向82
陸、 參考文獻84
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