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
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;
Conference_Titel :
Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on
Conference_Location :
Como
Print_ISBN :
0-7695-0619-4
DOI :
10.1109/IJCNN.2000.859364
