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ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams
Como, Italy July 24-July 27
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859364IEEE-INNS-ENNS International Joint Co ...
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Da Deng, Nikola Kasabov, "ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams," Neural Networks, IEEE - INNS - ENNS International Joint Conference on, vol. 6, pp. 6003, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000.
 
 
BibTex
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@article{ 10.1109/IJCNN.2000.859364,
author = {Da Deng and Nikola Kasabov},
title = {ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams},
journal ={Neural Networks, IEEE - INNS - ENNS International Joint Conference on},
volume = {6},
year = {2000},
issn = {1098-7576},
pages = {6003},
doi = {http://doi.ieeecomputersociety.org/10.1109/IJCNN.2000.859364},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
TI - ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams
SN - 1098-7576
SP
EP
A1 - Da Deng,
A1 - Nikola Kasabov,
PY - 2000
VL - 6
JA - Neural Networks, IEEE - INNS - ENNS International Joint Conference on
ER -
 
Da Deng, University of Otago
Nikola Kasabov, University of Otago
An algorithm of evolving self-organizing map (ESOM) is proposed as a dynamic version of the Kohonen self-organizing map, where network structure is evolved in an on-line adaptive mode. Experiments have been carried out on some benchmark data sets as well as on macroeconomic data. Results show that ESOM is a good tool for clustering, data analysis, and visualization.
Citation:
Da Deng, Nikola Kasabov, "ESOM: An Algorithm to Evolve Self-Organizing Maps from On-Line Data Streams," ijcnn, vol. 6, pp.6003, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 6, 2000
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