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Author(s): He, J. | Tan, A.H. | Tan, C.L. |
Year: 2004
Citation: IEEE TRANSACTIONS ON NEURAL NETWORKS Volume: 15 Issue: 3 Pages: 728-737
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.
Topics:
Machine Learning,
Applications:
Other,
Models:
ART 2-A,
