Modified ART 2A growing network capable of generating a fixed number of nodes.
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.
He
Ji
J
School of Computing, National University of Singapore, Singapore 117543. mail@jihe.net
Tan
Ah-Hwee
AH
Tan
Chew-Lim
CL
D016000:Cluster Analysis; D016571:Neural Networks, Computer