Neural computation and self-organizing maps: an introduction
Neural computation and self-organizing maps: an introduction
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
High Performance Data Mining Using the Nearest Neighbor Join
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Simultaneous Co-segmentation and Predictive Modeling for Large, Temporal Marketing Data
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
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This paper covers a comparison between two distinct approaches to neural network modeling. The first one is based on a developing of a single neural network model to predict bad debt events. The second one is based on combined models, building firstly a clustering model to recognize the pattern assigned to the customers, with a particular focus on the insolvency, and then developing several distinct neural networks to predict bad debt. In the second approach, for each group identified by the clustering model one neural network had been constructed. In that way, we turned the quite heterogeneous customer base more homogeneous, increasing the average accuracy for the predictive modeling once several straightforward models were built.