Home▸Data Science▸UMAP Layout Optimizer: Attractive & Repulsive Forces

UMAP Layout Optimizer: Attractive & Repulsive Forces (2D)

Watch UMAP's force-directed embedding optimizer run live in 2D: k-nearest-neighbor edges computed from a real 6-dimensional dataset pull connected points together while sampled non-neighbor pairs repel, converging from random noise into separated clusters. Tune n_neighbors, min_dist and repulsion strength and watch stress and neighbor-preservation react.

Data Science2DAdvanced60 FPS📱 Mobile-adapted⇄ 3D version

UMAP reduces high-dimensional data to a handful of dimensions by optimizing a low-dimensional layout so that its k-nearest-neighbor graph structure is preserved — using an attractive force along graph edges and a sampled inverse-square repulsive force between non-neighbor pairs, exactly the two-force tug-of-war visualized here in real time on a flat 2D canvas. Four synthetic clusters, generated in a real 6-dimensional space, start as random noise and are pulled and pushed into shape epoch by epoch; tune n_neighbors, min_dist, repulsion strength and optimizer speed while live readouts track layout stress and how much of the original 6D neighbor structure survives the 2D embedding.

⚙ Under the hood

Watch UMAP's force-directed embedding optimizer run live in 2D: k-nearest-neighbor graph edges pull connected points together while every pair repels with an inverse-square force, converging from random noise into separated clusters.

umapdimensionality-reductionforce-directed-layoutk-nearest-neighborsunsupervised-learningembedding

2D · HTML5 Canvas 2D · 60 FPS target · runs fully client-side, no install