Systematically determine `min_dist` and `n_neighbors`

I tested multiple combination of min_dist and n_neighbor on my data, I found that a suitable combination of those hyperparameters can separate the all the cluster(according to labels). I wonder how to determine those hyperparameters systematically, not by visualization of the data after embedding.

[lmcinnes]

The n_neighbors parameter in UMAP and the n_samples parameter in DBSCAN/HDBSCAN mean different things. They are, at least, measured in the same units. There is some reason to believe one could construct a clustering algorithm that would make use of the UMAP n_neighbors in a similar way to DBSCAN/HDBSCAN. As it stands however, I wouldn’t say they should be tied or linked. I would say, however, that they should probably be on roughly the same scale (I would expect them to be the same order of magnitude in general, for instance).

[MattWenham]

Start here for the best description of the algorithm: https://hal.archives-ouvertes.fr/hal-01461451

My implementation use somoclu as the SOM engine. It’s trapped in a Jupyter notebook at the moment, but I will try to get that shared at some point soon.