Comparative analysis of fuzzy ART and ART-2A network clustering performance

@article{Frank1998ComparativeAO,
  title={Comparative analysis of fuzzy ART and ART-2A network clustering performance},
  author={T. Frank and Karl-Friedrich Kraiss and Torsten Kuhlen},
  journal={IEEE transactions on neural networks},
  year={1998},
  volume={9 3},
  pages={544-59}
}
Adaptive resonance theory (ART) describes a family of self-organizing neural networks, capable of clustering arbitrary sequences of input patterns into stable recognition codes. Many different types of ART-networks have been developed to improve clustering capabilities. In this paper we compare clustering performance of different types of ART-networks: Fuzzy ART, ART 2A with and without complement encoded input patterns, and an Euclidean ART 2A-variation. All types are tested with two- and high… CONTINUE READING
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