This is very specific to 2D data. I bet the story is a lot different for high-dimensional data. The challenges you encounter with this sort of clumping in 2D is unlikely to occur in high-dimensional data due to the "curse" of dimensionality. Clustering in high dimensions has its own quirks and gotchas too, but they're quite distinct from the gotchas of low-dimensionality. Here's more from an actual expert: http://res…
The examples are all in 2D because it allows someone to visualise what's going on. In higher dimensions things get messier and you have to rely on cluster quality measures ... which are often bad (or, more often, defined to be the objective function that a particular clustering algorithm optimizes, and hence give a false sense of how "well" the clustering has done). I've worked with HDBSCAN quite successfully on mid-…
Re: Comparing Clustering Algorithms
#41Or to apply neural nets to compute some kind of vector representation that contains semantic information.