Modified Kohonen network algorithm for selection of the initial centres of Gustafson-Kessel algorithm in credit scoring
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 implem...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Universiti Putra Malaysia Press
2017
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| Online Access: | http://psasir.upm.edu.my/id/eprint/51605/1/06%20JST%20Vol%2025%20%281%29%20Jan%20%202017_0591-2015_pg77-90.pdf http://psasir.upm.edu.my/id/eprint/51605/ http://www.pertanika.upm.edu.my/Pertanika%20PAPERS/JST%20Vol.%2025%20(1)%20Jan.%202017/06%20JST%20Vol%2025%20(1)%20Jan%20%202017_0591-2015_pg77-90.pdf |
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| Summary: | 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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