Modified ART 2A growing network capable of generating a fixed number of nodes

Author

He, Ji ; Tan, Ah-Hwee ; Tan, Chew-Lim

Author_Institution

Sch. of Comput., Nat. Univ. of Singapore, Singapore

Volume

15

Issue

3

fYear

2004

fDate

5/1/2004 12:00:00 AM

Firstpage

728

Lastpage

737

Abstract

This paper introduces the Adaptive Resonance Theory under Constraint (ART-C 2A) learning paradigm based on ART 2A, which is capable of generating a user-defined number of recognition nodes through online estimation of an appropriate vigilance threshold. Empirical experiments compare the cluster validity and the learning efficiency of ART-C 2A with those of ART 2A, as well as three closely related clustering methods, namely online K-Means, batch K-Means, and SOM, in a quantitative manner. Besides retaining the online cluster creation capability of ART 2A, ART-C 2A gives the alternative clustering solution, which allows a direct control on the number of output clusters generated by the self-organizing process.

Keywords

ART neural nets; learning (artificial intelligence); self-adjusting systems; self-organising feature maps; SOM; adaptive resonance theory under constraint; batch K-Means; cluster validity; modified ART 2A growing network; neural networks; node generation; online estimation; recognition nodes; vigilance threshold; Clustering methods; Computer architecture; Constraint theory; Encoding; Helium; Neural networks; Neurons; Pattern recognition; Resonance; Subspace constraints; Cluster Analysis; Neural Networks (Computer);

fLanguage

English

Journal_Title

Neural Networks, IEEE Transactions on

Publisher

ieee

ISSN

1045-9227

Type

jour

DOI

10.1109/TNN.2004.826220

Filename

1296698