Sign up & Download
Sign in

Growing Hierarchical Tree SOM: An unsupervised neural network with dynamic topology

by Alberto Forti, Gian Luca Foresti
Neural Networks ()

Abstract

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. ?? 2006 Elsevier Ltd. All rights reserved.

Cite this document (BETA)

Available from Neural Networks
Page 1
hidden
Page 2
hidden

Readership Statistics

24 Readers on Mendeley
by Discipline
 
 
 
by Academic Status
 
21% Ph.D. Student
 
17% Student (Bachelor)
 
17% Associate Professor
by Country
 
8% Germany
 
4% Brazil
 
4% Slovenia

Sign up today - FREE

Mendeley saves you time finding and organizing research. Learn more

  • All your research in one place
  • Add and import papers easily
  • Access it anywhere, anytime

Start using Mendeley in seconds!

Already have an account? Sign in