Batch self-organizing maps for mixed feature-type symbolic data

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.