Clustering quality and topology preservation in fast learning SOMs

Abstract

The Self-Organizing Map (SOM) is a popular unsupervised neural network able to provide effective clustering and data visualization for multidimensional input spaces. Fast Learning SOM (FLSOM) adopts a learning algorithm that improves the performance of the standard SOM with respect to the convergence time in the training phase. In this paper we show that FLSOM also improves the quality of the map by providing better clustering quality and topology preservation of multidimensional input data. Several tests have been carried out on different multidimensional datasets, which demonstrate the superiority of the algorithm in comparison with the original SOM. © Springer-Verlag Berlin Heidelberg 2008.