HDBSCAN parameters about dbscan

[peekxc]

Hi @kbarylyuk,

Regarding the min_samples parameter:

The approach taken by the package right now is a bit different from SciKit's. In short, you are right, there are two 'min*' parameters associated with HDBSCAN. The first is minPts, which determines how the HDBSCAN hierarchy is made, i.e.

x <- as.matrix(iris[, 1:4])
cl <- dbscan::hdbscan(x, minPts = 5L)

The second, what you refer too as min_samples, is set by default to whatever the initial setting of minPts was, i.e. min_samples = minPts, however it can be given an alternative setting as follows:

dbscan::extractFOSC(cl$hc, minPts = <min_cluster_size>)

I recommend perusing the members of the cl object returned by HDBSCAN. It contains several members which may be useful. One of which is the hc element, which is an hclust object representing the HDBSCAN hierarchy. This hierarchy is what is parsed through via extractFOSC to determine the resulting clustering.

Regarding the cluster selection option:

As of right now, hdbscan only supports optimizing the excess of mass functional. I'm not sure what other cluster selection methods are in the Python one. However, it's worth noting that the hc element that was used above is indeed a valid hclust object. hclust objects natively have conversions to dendrogram objects as well, so, any tools you find that work off of either hclust or dendrogram objects in the R world you can use w/ HDBSCAN as well. For example, you can cut the tree like you might do with other hierarchical clustering algorithms (see ?stats::cutree), you can use the few cluster validation indices built for hierarchical clustering, and you can do a lot of dendrogram enhancements and statistics with packages like dendextend

[kbarylyuk]

Hi @peekxc,

thank you very much for these suggestions. I'll explore these options and see if they allow me to do what I want.