Multilayer feedforward networks are universal approximators
Neural Networks
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Neural network design
Sammon's mapping using neural networks: a comparison
Pattern Recognition Letters - special issue on pattern recognition in practice V
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
Neural Smithing: Supervised Learning in Feedforward Artificial Neural Networks
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Neural Networks: Tricks of the Trade, this book is an outgrowth of a 1996 NIPS workshop
Topology for Computing (Cambridge Monographs on Applied and Computational Mathematics)
Topology for Computing (Cambridge Monographs on Applied and Computational Mathematics)
Clustering: A neural network approach
Neural Networks
Baby morse theory in data analysis
Proceedings of the 2011 workshop on Knowledge discovery, modeling and simulation
Methodological triangulation using neural networks for business research
Advances in Artificial Neural Systems
Conditional fuzzy clustering in the design of radial basis function neural networks
IEEE Transactions on Neural Networks
Artificial neural networks for feature extraction and multivariate data projection
IEEE Transactions on Neural Networks
Advances in Artificial Neural Systems
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This paper develops a process whereby a high-dimensional clustering problem is solved using a neural network and a lowdimensional cluster diagram of the results is produced using the Mapper method from topological data analysis. The lowdimensional cluster diagram makes the neural network's solution to the high-dimensional clustering problem easy to visualize, interpret, and understand. As a case study, a clustering problem froma diabetes study is solved using a neural network. The clusters in this neural network are visualized using the Mapper method during several stages of the iterative process used to construct the neural network. The neural network and Mapper clustering diagram results for the diabetes study are validated by comparison to principal component analysis.