Paper Info
Title
Dual vigilance fuzzy adaptive resonance theory.
Abstract
Clusters retrieved by generic Adaptive Resonance Theory (ART) networks are limited to their internal categorical representation. This study extends the capabilities of ART by incorporating multiple vigilance thresholds in a single network: stricter (data compression) and looser (cluster similarity) vigilance values are used to obtain a many-to-one mapping of categories-to-clusters. It demonstrates this idea in the context of Fuzzy ART, presented as Dual Vigilance Fuzzy ART (DVFA), to improve the ability to capture clusters with arbitrary geometry. DVFA outperformed Fuzzy ART for the datasets in our experiments while yielding a statistically-comparable performance to another more complex, multi-prototype Fuzzy ART-based architecture.
Year
DOI
Venue
2019
10.1016/j.neunet.2018.09.015
Neural Networks
Keywords
DocType
Volume
Clustering,Adaptive resonance theory,ART,Visual assessment of cluster tendency,Topology,Unsupervised
Journal
109
Issue
ISSN
Citations 
1
0893-6080
2
PageRank 
References 
Authors
0.37
0
3
Name
Order
Citations
PageRank
Leonardo Enzo Brito da Silva193.31
Islam El-Nabarawy232.09
Wunsch II Donald C.3135491.73