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

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 vali...

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Bibliographic Details
Main Authors: HE, Ji, TAN, Ah-hwee, TAN, Chew-Lim
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2004
Subjects:
Adaptive Resonance Theory (ART)
clustering
constraint learning
neural networks
Computer Engineering
Databases and Information Systems
OS and Networks
Online Access:https://ink.library.smu.edu.sg/sis_research/5238
https://ink.library.smu.edu.sg/context/sis_research/article/6241/viewcontent/ARTC_TNN04.pdf
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Institution: Singapore Management University
Language: English
Description
Summary: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.

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