A Set-Medoids Vector Batch SOM Algorithm Based on Multiple Dissimilarity Matrices

This paper gives a batch SOM algorithm that is able to training a Kohonen map taking into account simultaneously several dissimilarity matrices, that are obtained using different sets of variables and dissimilarity functions. This algorithm is designed to provide a partition and a set-medoids vector representative for each cluster, and learn a relevance weight on the training for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each algorithm´s iteration and are different from one cluster to another. The proposed algorithm provides a collaborative role of the different dissimilarity matrices, aiming to cluster and visualizing the data while preserving their topology. Several examples illustrate the usefulness of the proposed algorithm.