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

Authors:
Ji He;Ah-Hwee Tan;Chew-Lim Tan
Affiliations:
Sch. of Comput., Nat. Univ. of Singapore, Singapore;-;-
Venue:
IEEE Transactions on Neural Networks
Year:
2004

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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.