A New Self-Organizing Map for Dissimilarity Data

Abstract

Adaptation of the Self-Organizing Map to dissimilarity data is of a growing interest. For many applications, vector representation is not available and but only proximity data (distance, dissimilarity, similarity, ranks ...). In this article, we present a new adaptation of the SOM algorithm which is compared with two existing ones. Three metrics for quality estimate (quantization and neighborhood) are used for comparison. Numerical experiments on artificial and real data show the algorithm quality. The strong point of the proposed algorithm comes from a more accurate prototype estimate which is one of the difficult parts of Dissimilarity SOM algorithms (DSOM).