# How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?

arXiv: https://arxiv.org/abs/1407.0611 · HTML: https://mlsanity.com/paper/1407.0611/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Jul 2014.

## Built on by (most cited first, 3 of 3)

- [Non-Euclidean Self-Organizing Maps](https://mlsanity.com/paper/2109.11769/) (2021)
- [Feature graphs for interpretable unsupervised tree ensembles: centrality, interaction, and application in disease subtyping](https://mlsanity.com/paper/2404.17886/) (2024)
- [Characteristics of networks generated by kernel growing neural gas](https://mlsanity.com/paper/2308.08163/) (2023)

## Abstract (arXiv)

In numerous applicative contexts, data are too rich and too complex to be represented by numerical vectors. A general approach to extend machine learning and data mining techniques to such data is to really on a dissimilarity or on a kernel that measures how different or similar two objects are. This approach has been used to define several variants of the Self Organizing Map (SOM). This paper reviews those variants in using a common set of notations in order to outline differences and similarities between them. It discusses the advantages and drawbacks of the variants, as well as the actual relevance of the dissimilarity/kernel SOM for practical applications.