Title :
Lazy Self-Organizing Map and its behaviors
Author :
Haraguchi, Taku ; Matsushita, Haruna ; Nishio, Yoshifumi
Author_Institution :
Dept. of Electr. & Eng., Univ. of Tokushima, Tokushima
Abstract :
The Self-Organizing Map (SOM) is a famous algorithm for the unsupervised learning and visualization introduced by Teuvo Kohonen. This study proposes the Lazy Self-Organizing Map (LSOM) algorithm which reflects the world of worker ants. In LSOM, three kinds of neurons exist: worker neurons, lazy neurons and indecisive neurons. We apply LSOM to various input data set and confirm that LSOM can obtain a more effective map reflecting the distribution state of the input data than the conventional SOM.
Keywords :
self-organising feature maps; unsupervised learning; indecisive neurons; lazy neurons; lazy self-organizing map; unsupervised learning; worker neurons; Brain modeling; Clustering algorithms; Data mining; Data visualization; Ground penetrating radar; Neurons; Pattern analysis; Pattern recognition; Performance evaluation; Unsupervised learning;
Conference_Titel :
Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4244-1820-6
Electronic_ISBN :
1098-7576
DOI :
10.1109/IJCNN.2008.4634112
