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Bação, F., Lobo, V. and Painho, M. (2004) Geo-Self-Organizing Map (Geo-SOM) for Building and Exploring Homogenous Regions. In: Egenhofer, M.J., Freksa, C. and Miller, H.J., Eds., Geographical Information Science, Lecture Notes in Computer Science, Vol. 3234, Springer, Berlin, 22-37.

has been cited by the following article:

• TITLE: Customer Segmentation of Credit Card Default by Self Organizing Map

  AUTHORS: Hui Wu, Chang-Chun Wang

  KEYWORDS: Self Organizing Map, Clustering, Machine Learning, Credit Default

  JOURNAL NAME: American Journal of Computational Mathematics, Vol.8 No.3, September 3, 2018

  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.