Please use this identifier to cite or link to this item: https://doi.org/10.1109/TNN.2004.826220
Title: Modified ART 2A growing network capable of generating a fixed number of nodes
Authors: He, J. 
Tan, A.-H.
Tan, C.-L. 
Keywords: Adaptive Resonance Theory (ART)
Clustering
Constraint learning
Neural networks
Issue Date: 2004
Source: He, J., Tan, A.-H., Tan, C.-L. (2004). Modified ART 2A growing network capable of generating a fixed number of nodes. IEEE Transactions on Neural Networks 15 (3) : 728-737. ScholarBank@NUS Repository. https://doi.org/10.1109/TNN.2004.826220
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.
Source Title: IEEE Transactions on Neural Networks
URI: http://scholarbank.nus.edu.sg/handle/10635/43069
ISSN: 10459227
DOI: 10.1109/TNN.2004.826220
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