Modelling Municipal Rating by Cluster Analysis and Neural Networks
نویسنده
چکیده
The paper presents the design of the parameters for long-term municipal rating. Modelling of the rating is realized by means of unsupervised methods, because the rating classes are not known a priori. The model design based on statistical methods (neural networks) is represented by cluster analysis (self-organizing feature maps). Key-Words: Credit risk, rating, unsupervised learning, cluster analysis, K-means algorithm, neural networks, Kohonen’s self-organizing feature maps.
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