Share Email Print
cover

Proceedings Paper

Comparative study of the performance of fuzzy ART-type clustering algorithms in pattern recognition
Author(s): Yong Soo Kim; Sunanda Mitra
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper 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.

Paper Details

Date Published: 1 November 1992
PDF: 7 pages
Proc. SPIE 1826, Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods, (1 November 1992); doi: 10.1117/12.131612
Show Author Affiliations
Yong Soo Kim, Texas Tech Univ. (United States)
Sunanda Mitra, Texas Tech Univ. (United States)


Published in SPIE Proceedings Vol. 1826:
Intelligent Robots and Computer Vision XI: Biological, Neural Net, and 3D Methods
David P. Casasent, Editor(s)

© SPIE. Terms of Use
Back to Top