Before revision:  Here, we introduce a new data visualization and exploration method, TMAP (tree-map), which exploits locality sensitive hashing, Kruskal's minimum-spanning-tree algorithm, and a multilevel multipole-based graph layout algorithm to represent large and high dimensional data sets as a tree structure, which is readily understandable and explorable. Compared to other data visualization methods such as t-SNE or UMAP , TMAP increases the size of data sets that can be visualized due to its significantly lower memory requirements and running time and should find broad applicability in the age of big data.
After revision:  Here, we introduce a new data visualization and exploration method, TMAP , capable of representing data sets of up to millions of data points and arbitrary high dimensionality as a two-dimensional tree URL Visualizations based on TMAP are better suited than t-SNE or UMAP , TMAP increases the size of data sets that can be visualized due to its significantly lower memory requirements and running time and should find broad applicability in the age of big data.