Abstract
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.
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Tian, D., Ren, Y., Li, Q. (2008). Dynamic Growing Self-organizing Neural Network for Clustering. In: Tang, C., Ling, C.X., Zhou, X., Cercone, N.J., Li, X. (eds) Advanced Data Mining and Applications. ADMA 2008. Lecture Notes in Computer Science(), vol 5139. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88192-6_60
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DOI: https://doi.org/10.1007/978-3-540-88192-6_60
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