Comparative study of the performance of fuzzy ART-type clustering algorithms in pattern recognition

Yong Soo Kim; Sunanda Mitra

doi:10.1117/12.131612

1 November 1992 Comparative study of the performance of fuzzy ART-type clustering algorithms in pattern recognition

Yong Soo Kim, Sunanda Mitra

Proceedings Volume 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods; (1992) https://doi.org/10.1117/12.131612
Event: Applications in Optical Science and Engineering, 1992, Boston, MA, United States

Abstract

This paper presents an unsupervised fuzzy neural network which can be used for clustering and classification of complex data sets. The Integrated Adaptive Fuzzy Clustering (IAFC) architecture uses a control structure similar to that found in the Adaptive Resonance Theory (ART-1) with a new learning rule and a new similarity measure. We compare IAFC with other fuzzy ART-type clustering algorithms. The critical parameters in the operation of the IAFC are discussed. The Anderson's iris data are used to show the performance of the algorithm in comparison with other clustering algorithms.

Citation Download Citation

Yong Soo Kim and Sunanda Mitra "Comparative study of the performance of fuzzy ART-type clustering algorithms in pattern recognition", Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); https://doi.org/10.1117/12.131612

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