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研究生：	呂峻益 Leu, Jiunn-Yin
論文名稱：	以Fuzzy 分群技術以及知識管理認知落差的調校模式應用於銀行信貸管理作業 Adapted Fuzzy-Clustering Theory and the Knowledge Management Disparity Research to Bank Loans Management Policy
指導教授：	黃悅民 Huang, Yueh-Min
學位類別：	博士 Doctor
系所名稱：	工學院 - 工程科學系 Department of Engineering Science
論文出版年：	2013
畢業學年度：	101
語文別：	英文
論文頁數：	78
中文關鍵詞：	Fuzzy 分群技術、PZB 架構、模擬退火法
外文關鍵詞：	Fuzzy Cluster, PZB Model, Simulated Annealing
相關次數：	點閱：70 下載：0
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本研究主要探索金融產業貸款管理之知識認知落差，例如：金融機構主管可能想要維持一個低的逾放比率，但是，信用評估部門以及客服部門，卻想多增加放款以增加業績量，於是形成一種認知上的落差。

在這份研究中，首先是分群問題．由於受限於各金融單位職權跟職務名稱不一，而研究調查需以實際上的職權，來進行分群，例如：一些國營體系的金融單位，其相對於民營體系的金融單位，同樣的職稱，卻有較大的決策權力。因此，我們使用FUZZY分群機制，來確認統計分群前，每位問卷回答者真實的權限。在確認分群問題之後，根據各個群體所屬的實際權力級別，進行分群後的問卷統計分析，以驗證所假設的六個落差模型是否存在。最後，我們嘗試著用模擬退火法，去縮短驗過後的知識落差，根據實驗金融單位的內部資料，最後實際降低該金融機構7%-12%的逾放比.

The research explore the knowledge disparity which the employee examine loan in financial organization. For example in banking senior directors want maintain a low level of overdue loans and so establish policies to reflect this (e.g., high threshold checks for those applicants with bad credit). However the credit evaluation departments, operating under the influence of the Director of Customer Service are more inclined to allow everyone to apply for a loan.
In this study, we initially adopted the Fuzzy Clustering Method to group by the employees’ authority of office. Moreover, the research verifies the knowledge disparity by statistics method. Final, we build a prototype system by simulated annealing algorithm to test whether reduce the overdue bank loans by shorten the knowledge disparity which is between employee. Furthermore, according to the bank’s unpublished data, the use of our proposed system led to a 7-12% reduction in overdue bank loans.
Keywords：Fuzzy Cluster; PZB Model; Simulated Annealing

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Content
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摘要	I
Abstract	II
致謝頁	III
List of Tables	VI
List of Figures	VII
Chapter 1 Introduction	1
1.1	Fuzzy Clustering	1
1.2	Knowledge Management in Bank	4
1.3	The Research Framework	6
Chapter 2 Fuzzy Clustering	13
2.1 Introduction of Fuzzy Clustering	13
2.2 The Review of Fuzzy C-Means (FCM)	16
2.3 Initialization Procedures	18
Chapter 3 Develop the Novel Fuzzy Clustering Methods--MVDFCM and PEFCM	19
3.1	Means-Variable-Distance objective function of FCM (MVDFCM)	19
3.2	Positive-Entropy FCM [PEFCM]	24
3.2.1	Membership Partitioning	25
3.2.2	Cluster Prototype	28
3.3	Experiment Results	29
3.3.1.	Experiment Results on Synthetic Image	29
3.3.2.	Experimental results on real datasets	34
I.	Result on Check Board	35
II.	The Test on Lung Cancer Data Set	40
3.3.3.	Clustering	44
Chapter 4 Knowledge Management in Bank Loan Policy	46
4.1 Internal knowledge gaps model	49
4.2 Statistical Analysis	55
4.2.1	Sample	55
4.2.2	Knowledge Management Disparity and Cluster Analysis	57
4.3 System Development	61
4.3.1.	The Formulation	61
4.3.2.	Cloud Computing with Mobile Devices	66
4.4 Discussion	68
Chapter 5 Conclusion	71
Reference	73
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List of Tables
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Table.1	Clustering Results On Checkerboard and Lung Cancer Datasets. Minutes [M], Seconds[S], and Iterations [Its]	45
Table.2	Distribution of valid questionnaires	51
Table.3	The Structure  of  Distribution of valid questionnaires	55
Table.4	Reliability and validity measures	56
Table.5	reliability and consistency of scores	57
Table.6	Cluster analysis results	58
Table.7	Clusters in the level of knowledge management disparity and bank performance	59
Table.8	Correlation between knowledge management disparity and bank  performance	60
Table.9	Results of Simulation and Testing (1)	69
Table.10	Results of Simulation and Testing (2)	70
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List of Figures
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Fig.1	2D Artificial Dataset & 2D Synthetic Image	29
Fig.2	Comparison of segmentation results on synthetic test image	31
Fig.3	Centers’ Convergence	32
Fig.4	The clustering results on synthetic 2D image dataset.	32
Fig.5	Resulted membership values on synthetic images by FCM, MVDFCM and PEFCM	33
Fig.6	1000 Check board Dataset	35
Fig.7	Obtained clusters levels of memberships with silhouette width by FCM	36
Fig.8	Obtained clusters levels of memberships with silhouette by MVDFCM	37
Fig.9	Obtained clusters levels of memberships with silhouette by PEFCM	37
Fig.10	Location of the centers and corresponding separated classes	39
Fig.11	level lines classes and silhouette width - quality of cluster	42
Fig.12	Location of centers and allocated clusters obtained.	43
Fig.13	Internal knowledge gaps related to the banking	50
Fig.14	User Interface	66
Fig.15	System Framework of Cloud Services	66
                                    

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