A simple "possibilistic" clustering neural network

O. Yadid-Pecht, M. Gur

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

Abstract

A simple "possibilistic" clustering method, Le. clustering where each datum has a degree of possibility of belonging to the cluster, using a neural net, is suggested. The implementation consists of simple "neurons", requiring only a small number of local connections, collectively performing a diffusion-like process. In spite of its simplicity, this implementation has several advantages over commonly used fuzzy clustering methods. Specifically, it provides the "typicality" notion that is lacking in the well known Fuzzy C Means (FCM) and its derivatives, and is less sensitive to noise.

Original languageEnglish
Title of host publicationProceedings of the 12th IAPR International Conference on Pattern Recognition - Conference B
Subtitle of host publicationPattern Recognition and Neural Networks, ICPR 1994
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages520-521
Number of pages2
ISBN (Electronic)0818662700
StatePublished - 1 Jan 1994
Externally publishedYes
Event12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994 - Jerusalem, Israel
Duration: 9 Oct 199413 Oct 1994

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2
ISSN (Print)1051-4651

Conference

Conference12th IAPR International Conference on Pattern Recognition - Conference B: Pattern Recognition and Neural Networks, ICPR 1994
Country/TerritoryIsrael
CityJerusalem
Period9/10/9413/10/94

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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