Title :
Batch self-organizing maps for mixed feature-type symbolic data
Author :
de Carvalho, Francisco de A. T. ; Barbosa, Gibson B. N.
Author_Institution :
Center of Inf., Fed. Univ. of Pernambuco Recife, Recife, Brazil
Abstract :
The Kohonen Self-Organizing Map (SOM) is an unsupervised neural network method with a competitive learning strategy which has both clustering and visualization properties. In this paper, we present batch SOM algorithms based on adaptive and non-adaptive distances for mixed feature-type symbolic data that, for a fixed epoch, optimize a cost function. The performance, and usefulness of these SOM algorithms are illustrated with real mixed feature-type symbolic data sets.
Keywords :
data visualisation; pattern clustering; self-organising feature maps; unsupervised learning; Kohonen self-organizing map; batch SOM algorithms; batch self-organizing maps; clustering properties; competitive learning strategy; real mixed feature-type symbolic data sets; unsupervised neural network method; visualization properties; Cities and towns; Clustering algorithms; Cost function; Neurons; Prototypes; Temperature distribution; Vectors;
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-6128-6
DOI :
10.1109/IJCNN.2013.6706729
