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Modified Kohonen network algorithm for selection of the initial centres of Gustafson-Kessel algorithm in credit scoring


Citation

Sameer, Fadhaa Othman and Abu Bakar, Mohd Rizam (2017) Modified Kohonen network algorithm for selection of the initial centres of Gustafson-Kessel algorithm in credit scoring. Pertanika Journal of Science & Technology, 25 (1). pp. 77-90. ISSN 0128-7680; ESSN: 2231-8526

Abstract / Synopsis

Credit risk assessment has become an important topic in financial risk administration. Fuzzy clustering analysis has been applied in credit scoring. Gustafson-Kessel (GK) algorithm has been utilised to cluster creditworthy customers as against non-creditworthy ones. A good clustering analysis implemented by good Initial Centres of clusters should be selected. To overcome this problem of Gustafson-Kessel (GK) algorithm, we proposed a modified version of Kohonen Network (KN) algorithm to select the initial centres. Utilising similar degree between points to get similarity density, and then by means of maximum density points selecting; the modified Kohonen Network method generate clustering initial centres to get more reasonable clustering results. The comparative was conducted using three credit scoring datasets: Australian, German and Taiwan. Internal and external indexes of validity clustering are computed and the proposed method was found to have the best performance in these three data sets.


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Additional Metadata

Item Type: Article
Divisions: Faculty of Science
Publisher: Universiti Putra Malaysia Press
Keywords: Credit scoring; Decision-making; Clustering techniques; Fuzzy clustering algorithms; Gustafson-Kessel algorithm; Kohonen network
Depositing User: Nabilah Mustapa
Date Deposited: 30 Mar 2017 18:27
Last Modified: 30 Mar 2017 18:39
URI: http://psasir.upm.edu.my/id/eprint/51605
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