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Customer Segmentation of Credit Card Default by Self Organizing Map
Department of Mathematics, Clark Atlanta University, Atlanta, GA, USA
International Monetary Fund, Washington DC, USA
- 1 Department of Mathematics, Clark Atlanta University, Atlanta, GA, USA
- 2 International Monetary Fund, Washington DC, USA
American Journal of Computational Mathematics·Volume 08 (2018)·Pages 197–202·Published 31 August 2018·DOI10.4236/ajcm.2018.83015
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Abstract
In this paper we applied the technique of Self Organizing Map (SOM) to segment individuals based on their credit information. SOM is an unsupervised machine learning method that reduces data complexity and dimensionality while keeping sits original topology, which is superior to other dimension reduction methods especially when features in data have unclear nonlinear relations. Through this method we provide more clear and intuitive segmentation that other traditional methods cannot achieve.
KeywordsSelf Organizing MapClusteringMachine LearningCredit Default
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