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Please use this identifier to cite or link to this item: https://ptsldigital.ukm.my/jspui/handle/123456789/579098
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dc.contributor.authorSameer F
dc.contributor.authorAbu Bakar M. R (UPM)
dc.date.accessioned2023-11-06T03:14:38Z-
dc.date.available2023-11-06T03:14:38Z-
dc.date.issued2017-01
dc.identifier.issn0128-7680
dc.identifier.otherukmvital:116452
dc.identifier.urihttps://ptsldigital.ukm.my/jspui/handle/123456789/579098-
dc.descriptionCredit 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.
dc.language.isoen
dc.publisherUniversiti Putra Malaysia Press
dc.relation.haspartPertanika Journals
dc.relation.urihttp://www.pertanika.upm.edu.my/regular_issues.php?jtype=2&journal=JST-25-1-1
dc.rightsUKM
dc.subjectCredit Scoring
dc.subjectDecision-making
dc.subjectClustering Techniques
dc.subjectFuzzy Clustering Algorithms
dc.subjectGustafson-Kessel Algorithm
dc.subjectKohonen Network
dc.titleModified kohonen network algorithm for selection of the initial centres of gustafson-kessel algorithm in credit scoring
dc.typeJournal Article
dc.format.volume25
dc.format.pages77-90
dc.format.issue1
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