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@inproceedings{807561,
author = {Hudík, Tomáš and Žižka, Jan},
address = {Brno},
booktitle = {5. Letní škola aplikované informatiky},
edition = {1},
keywords = {Adaptive Resonance Theory; clustering; Machine Learning.},
language = {eng},
location = {Brno},
isbn = {978-80-210-4774-7},
pages = {49-59},
publisher = {Masarykova univerzita},
title = {Clustering with Various Distance Measures in Adaptive Resonance Theory},
year = {2008}
}
TY - JOUR
ID - 807561
AU - Hudík, Tomáš - Žižka, Jan
PY - 2008
TI - Clustering with Various Distance Measures in Adaptive Resonance Theory
PB - Masarykova univerzita
CY - Brno
SN - 9788021047747
KW - Adaptive Resonance Theory
KW - clustering
KW - Machine Learning.
N2 - The adaptive resonance theory (ART) was developed by psychologists some 20 years ago. Later on, it was adapted by machine learning for the supervised as well as unsupervised type of learning. In this article, we briefly explain ART-based unsupervised algorithms and the implementation of ART-based algorithms. We compare and discuss the performance of ART and some other well-known algorithms on various datasets. The basic features of the ART-based algorithms are explained, such as speed or how to build parameters' net to gain the global maxima (the best solution) - or some very good local maxima - automatically without the necessity of guessing the best input parameters' setting.
ER -
HUDÍK, Tomáš a Jan ŽIŽKA. Clustering with Various Distance Measures in Adaptive Resonance Theory. In \textit{5. Letní škola aplikované informatiky}. 1. vyd. Brno: Masarykova univerzita, 2008, s.~49-59. ISBN~978-80-210-4774-7.
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