Then, we propose a novel consensus clustering framework to uncover patterns of consumer behaviour which draws upon the ability of cluster ensembles to reveal robust clusters from diffcult datasets. Our Homals Consensus, models successfully the relationships between different clusterings in the cluster ensemble and manages to uncover representative clusters that are more suitable for explaining the complex patterns of a socio economic dataset. Finally under a supervised learning approach the behavioural aspects of consumer indebtedness are assessed.In more detail, we take advantage of the exibility Neural Networks provide in determining their architecture in order to propose a novel Neural Network solution, named TopDNN, that can handle non linearities in the data and takes into account the extracted behavioural knowledge by incorporating it in the model.
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