Context and scale influencing clustering through unsupervised neural networks

This work aims at proposing a neural network model for clustering in which no information about the desired output is given, and influences due to context and scale are considered. Two models of unsupervised neural networks are described. The first is a winner-take-all (WTA) algorithm with pre-established lateral inhibition, the second is a model with Hebbian and anti-Hebbian learning. Both models have the same architecture but the second one has adaptable lateral inhibitory links. The proposed models are used in two different domains: classification of the iris and classification of animals. In the first, the patterns are formed by continuous inputs, while in the second, the inputs are mainly binary. The proposed models are evaluated according to their capacity of generalization, ability to classify nonlinearly separable patterns and robustness when clustering noisy patterns