Title :
Hierarchical clustering with ART neural networks
Author_Institution :
Dept. of Comput. Sci., Victoria Univ., Wellington, New Zealand
fDate :
27 Jun-2 Jul 1994
Abstract :
This paper introduces the concept of a modular neural network structure, which is capable of clustering input patterns through unsupervised learning, and representing a self-consistent hierarchy of clusters at several levels of specificity. In particular, we use the ART neural network as a building block, and name our architecture SMART (for Self-consistent Modular ART). We also show some experimental results for “proof-of-concept” using the ARTMAP network, that can be seen as an implementation of a two-level SMART network
Keywords :
ART neural nets; parallel architectures; pattern recognition; unsupervised learning; ART neural networks; hierarchical clustering; input patterns clustering; modular neural network structure; self-consistent hierarchy; unsupervised learning; Animal structures; Computer science; Humans; Intelligent sensors; Intelligent systems; Neural networks; Neurons; Probability distribution; Subspace constraints; Unsupervised learning;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374307
