Growing hierarchical tree SOM: an unsupervised neural network with 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.
Forti
Alberto
A
Department of Mathematics and Computer Science (DIMI), University of Udine - Via delle Scienze 208-33100 Udine, Italy. forti@fisica.uniud.it
Foresti
Gian Luca
GL
D000465:Algorithms; D015870:Gene Expression / Q000502:physiology; D006801:Humans; D016247:Information Storage and Retrieval; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics