This! We do it all the time in fraud, genomics, social media, security, etc data tables, it is one of our favorite techniques.

We do one more step for UMAP and other dimensionality reducers in practice: connect the nearest neighbors to turn any data table into an explorable similarity graph. Users, devices, proteins, accounts, events, etc link together and cluster when similar, and this reveals fascinating structural relationships when there is a reason they vary.

Takes just a few lines once you have a feature table, and there's a recent GH issue to automate that step too: https://github.com/graphistry/pygraphistry/blob/master/demos...