Self-organizing maps (SOMs) have many advantages as a tool for exploratory data analysis. Combining vector quantization and topological relationships that are defined in a low-dimensional space, they can run on big data sets and are mostly immune to the curse of dimensionality in the data space.

SOMs are used mainly for dimensionality reduction and marginally for clustering; however, SOMs also suffer from some shortcomings.

Vector quantization makes them unable to embed all data points, only prototypes or centroid are mapped.

Being defined as a regular grid in the low-dimensional space, dimensionality reduction and clustering with SOMs are indirect, as compared to methods of direct embedding like multi-dimensional scaling.

Since 2008, t-SNE (t-distributed stochastic neighbor embedding) has raised growing interest, first in the machine learning community and now outside of it, with many applications in cell biology, for instance.

Primarily used as a 2D embedding and visualization technique, t-SNE is more and more used as a clustering technique, which is capable of identifying meaningful clusters that classical clustering tools struggle to see.

Quite counter-intuitively, t-SNE often better separates clusters in low-dimensional embeddings than clustering tools would do in the high-dimensional data space.

To understand this paradox, several mechanisms of t-SNE can be framed as a distance transformation with the possibility (i) to denoise distances in high-dimensional spaces and (ii) to impose a strong inductive bias on them, which magnifies inter-cluster gaps.

Despite these strengths, t-SNE is not free of drawbacks, which we quickly review to sketch perspectives of future developments.