Please use this identifier to cite or link to this item: https://scholarhub.balamand.edu.lb/handle/uob/2048
Title: Geometrical-Based Category Choice Fuzzy Art Architecture
Authors: Dagher, Issam 
Affiliations: Department of Computer Engineering 
Keywords: Fuzzy ART
L-1 norm
Category choice
Complement coding clustering
Issue Date: 2007
Part of: International journal of modeling and simulation
Volume: 27
Issue: 2
Start page: 158
End page: 163
Abstract: 
In this paper, the category choice of the Fuzzy adaptive resonance theory (ART) is shown to be replaced by a distance measure related to the L-1 norm. This replacement has several advantages. One advantage is that the new distance measure will operate directly on the input patterns without the need for doing complement coding which is a requirement for the category choice of the Fuzzy ART. Another advantage is that using the new distance measure the input patterns do not have to be normalized to be clustered. It is noted that using Fuzzy ART the input patterns need to be in the interval [0, 1]. The difference between the two distance measures is illustrated mathematically and geometrically. Simulation results on different databases are presented.
URI: https://scholarhub.balamand.edu.lb/handle/uob/2048
DOI: 10.1080/02286203.2007.11442412
Ezproxy URL: Link to full text
Type: Journal Article
Appears in Collections:Department of Computer Engineering

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