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

Yong Soo Kim, Sunanda Mitra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Pages335-341
Number of pages7
ISBN (Print)0819410276
StatePublished - 1993
EventIntelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods - Boston, MA, USA
Duration: Nov 18 1992Nov 20 1992

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume1826
ISSN (Print)0277-786X

Conference

ConferenceIntelligent Robots and Computer Vision XI: Biological, Neural Net, and 3-D Methods
CityBoston, MA, USA
Period11/18/9211/20/92

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