A Game-Theoretic Adaptive Categorization Mechanism for ART-Type Networks
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
Analyses on the Generalised Lotto-Type Competitive Learning
IDEAL '00 Proceedings of the Second International Conference on Intelligent Data Engineering and Automated Learning, Data Mining, Financial Engineering, and Intelligent Agents
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Adaptive double self-organizing maps for clustering gene expression profiles
Neural Networks - 2003 Special issue: Advances in neural networks research IJCNN'03
A feature extraction unsupervised neural network for an environmental data set
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
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Neural Networks have been widely used in the field of intelligent information processing such as classification, clustering, prediction, and recognition. Unsupervised learning is the main method to collect and find features from large unlabeled data. In this paper a new unsupervised learning clustering neuron network--Dynamic Growing Self-organizing Neuron Network (DGSNN) is presented. It uses a new competitive learning rule--Improved Winner-Take-All (IWTA) and adds new neurons when it is necessary. The advantage of DGSNN is that it overcomes the usual problems of other clustering methods: dead units and prior knowledge of the number of clusters. In the experiments, DGSNN is applied to clustering tasks to check its ability and is compared with other clustering algorithms RPCL and WTA. The results show that DGSNN performs accurately and efficiently.