期刊
NEURAL NETWORKS
卷 19, 期 10, 页码 1568-1580出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.neunet.2006.02.009
关键词
unsupervised learning; self-organization; clustering; growing neural networks; dynamic topology
In this paper we introduce a tree structured self-organizing network, called the Growing Hierarchical Tree SOM (GHTSOM), that combines unsupervised learning with a dynamic topology for hierarchical classification of unlabelled data sets. The main feature of the proposed model is a SOM-like self-organizing process that allows the network to adapt the topology of each layer of the hierarchy to the characteristics of the training set. In particular the self-organization is obtained in two steps: the first one concerns the learning phase and is finalized with the creation of a tree of SOMs, while the second one is in regard to the clustering phase and provides the formation of classes for each level of the tree (hence self-organization not only for training but also for the creation of topological connections). As a result the network works without the need for user-defined parameters. Experimental results are proposed on both synthetic and real data sets. (c) 2006 Elsevier Ltd. All rights reserved.
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