Influence of learning rates and neighboring functions on self-organizing maps

Abstract

In the article, the influence of neighboring functions and learning rates on self-organizing maps (SOM) has been investigated. The target of a self-organizing map is data clustering and their graphical presentation. Bubble, Gaussian, and heuristic neighboring functions and four learning rates (linear, inverse-of-time, power series, and heuristics) have been analyzed here. The learning rate has been changed according to epochs and iterations. A comparative analysis has been made with three data sets: glass, wine, and zoo. The quantization error has been measured in order to estimate the SOM quality. © 2011 Springer-Verlag Berlin Heidelberg.