Clustering of noisy image data using an adaptive neuro-fuzzy system

S. Pemmaraju, Sunanda Mitra

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

Abstract

Identification of outliers or noise in a real data set is often quite difficult. A recently developed adaptive fuzzy leader clustering (AFLC) algorithm has been modified to separate the outliers from real data sets while finding the clusters within the data sets. The capability of this modified AFLC algorithm to identify the outliers in a number of real data sets indicates the potential strength of this algorithm in correct classification of noise real data.

Original languageEnglish
Title of host publicationProceedings of SPIE - The International Society for Optical Engineering
PublisherPubl by Int Soc for Optical Engineering
Pages327-334
Number of pages8
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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