Welcome to the t-Distributed Stochastic Neighbor Embedding (T-SNE) module of R2. T-SNE models each high-dimensional object by a two-dimensional point in such a way that similar objects are modeled by nearby points and dissimilar objects are modeled by distant points.
An important parameter in T_SNE is the 'perplexity', a value which kind of reflect the number of close neigbours.
R2 will scan a whole range of these and allow you to 'browse' through them. Because of this, a T-SNE run can take a long time to finish (up to an hour for ~500 samples). Within R2, a fixed seed (fixed random number) is used to generate reproducible results.
T-SNE plots often look pretty, however be sure to understand some of the basic properties before you interpret the result. We can warmly recommend the following blog post on T-SNE behaviour here
In this section, only datasets for which a complete analysis has been executed are listed for visualization and inspection. Depending on your access rights, t-SNE can also be executed from box3.