Adaptive Batch SOM for Multiple Dissimilarity Data Tables

This paper introduces a clustering algorithm based on batch Self-Organizing Maps to partition objects taking into account their relational descriptions given by multiple dissimilarity matrices. The presented approach provides a partition of the objects and a prototype for each cluster, moreover the method is able to learn relevance weights for each dissimilarity matrix by optimizing an adequacy criterion that measures the fit between clusters and the respective prototypes. These relevance weights change at each iteration and are different from one cluster to another.