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Customer Segmentation of Credit Card Default by Self Organizing Map

机译:通过自组织图对信用卡违约的客户细分

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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.
机译:在本文中,我们应用了自组织地图(SOM)技术根据个人信用信息对个人进行细分。 SOM是一种无监督的机器学习方法,可在保持原始拓扑结构的同时降低数据复杂性和维数,这优于其他维数缩减方法,尤其是当数据中的特征具有不清楚的非线性关系时。通过这种方法,我们提供了其他传统方法无法实现的更清晰直观的细分。

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