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