DocumentCode
2285610
Title
ESOM: an algorithm to evolve self-organizing maps from online data streams
Author
Da Deng ; Kasabov, Nikola
Author_Institution
Dept. of Inf. Sci., Otago Univ., Dunedin, New Zealand
Volume
6
fYear
2000
fDate
2000
Firstpage
3
Abstract
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 online 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 visualisation
Keywords
data analysis; data visualisation; learning (artificial intelligence); real-time systems; self-organising feature maps; ESOM; Kohonen SOM; clustering; data analysis; data visualisation; macroeconomic data; online adaptive mode; online learning; self-organizing map; Artificial intelligence; Computational modeling; Data analysis; Data visualization; Electronic mail; Information science; Learning systems; Macroeconomics; Prototypes; Self organizing feature maps;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location
Como
ISSN
1098-7576
Print_ISBN
0-7695-0619-4
Type
conf
DOI
10.1109/IJCNN.2000.859364
Filename
859364
Link To Document