A Sequential Algorithm for Training the SOM Prototypes Based on Higher-Order Recursive Equations

Abstract

A novel training algorithm is proposed for the formation of Self-Organizing Maps (SOM). In the proposed model, the weights are updated incrementally by using a higher-order difference equation, which implements a low-pass digital filter. It is possible to improve selected features of the self-organization process with respect to the basic SOM by suitably designing the filter. Moreover, from this model, new visualization tools can be derived for cluster visualization and for monitoring the quality of the map.

Pattern Clustering Using Ants Colony, Ward Method and Kohonen Maps

“…The Multivariate Statistics area method (Ward Method) was used because it is one of the most classical methods in the literature (Johnson and Wichern, 1998). On their turn, the One-dimensional Kohonen Maps were used because, like the Ant-based Clustering, they perform simultaneously the clustering and topographic mapping tasks (Raug and Tucci, 2010); (Kohonen, 2001).…”