On-line relational SOM for dissimilarity data

TL;DR

This paper introduces an online relational Self-Organizing Map algorithm designed for dissimilarity data, improving upon previous batch methods by enhancing topographic organization and computational efficiency.

Contribution

It presents a novel online version of relational SOM that addresses complexity issues and improves topographic quality for non-vector data.

Findings

The online relational SOM performs better than batch versions in topographic organization.

It is effective on categorical data and graphs.

The algorithm is computationally more efficient.

Abstract

In some applications and in order to address real world situations better, data may be more complex than simple vectors. In some examples, they can be known through their pairwise dissimilarities only. Several variants of the Self Organizing Map algorithm were introduced to generalize the original algorithm to this framework. Whereas median SOM is based on a rough representation of the prototypes, relational SOM allows representing these prototypes by a virtual combination of all elements in the data set. However, this latter approach suffers from two main drawbacks. First, its complexity can be large. Second, only a batch version of this algorithm has been studied so far and it often provides results having a bad topographic organization. In this article, an on-line version of relational SOM is described and justified. The algorithm is tested on several datasets, including categorical data and graphs, and compared with the batch version and with other SOM algorithms for non vector data.