Acc\'el\'eration des cartes auto-organisatrices sur tableau de dissimilarit\'es par s\'eparation et \'evaluation

TL;DR

This paper introduces a faster implementation of Kohonen self-organising maps adapted for dissimilarity matrices, using branch and bound to reduce computation time without altering the results.

Contribution

It presents a novel branch and bound-based method for accelerating SOM adaptation to dissimilarity data, maintaining exact output.

Findings

Significant reduction in algorithm running time

Exact equivalence to standard SOM results

Applicable to dissimilarity matrix data

Abstract

In this paper, a new implementation of the adaptation of Kohonen self-organising maps (SOM) to dissimilarity matrices is proposed. This implementation relies on the branch and bound principle to reduce the algorithm running time. An important property of this new approach is that the obtained algorithm produces exactly the same results as the standard algorithm.