Thanks Ryan. It seems maybe something like HAC or HDBScan maybe better for my needs.
On Sun, Dec 30, 2018 at 11:51 PM Ryan Curtin <[email protected]> wrote: > On Sun, Dec 30, 2018 at 11:42:54PM -0500, Tharindu Mathew wrote: > > Hi, > > > > Would someone kindly point me to what range and point selection types are > > available for dbscan? Going through header or API docs didn't help in > this > > case. > > Hey there Tharindu, > > I dug into it a little bit after seeing your IRC messages. You can see > my responses here: > > http://mlpack.org/irc/mlpack.20181230.html > > Actually I ended up noticing that PointSelectionPolicy isn't even used, > so I opened an issue about it: > > https://github.com/mlpack/mlpack/issues/1625 > > For the RangeSearchType, that is more there in case someone wants to use > a different algorithm for the range search portion of the algorithm. > Right now mlpack::range::RangeSearch is the only option implemented in > the library. The results it will give are exact, so the only thing that > would make a difference is if you, e.g., wanted to use RangeSearch with > a different metric (RangeSearch itself has some template parameters), or > replace it with a different algorithm that might run faster in your case > or something (but like I said you'd need to hand-implement it, and it > would just need to have a Search() function matching the API of > RangeSearch). > > The biggest parameters that will make a difference for the DBSCAN > clustering are epsilon and minPoints. You might also consider trying > some of the other clustering algorithms in mlpack, like MeanShift and > KMeans. > > Hope this helps! > > -- > Ryan Curtin | "This room is green." > [email protected] | - Kazan > _______________________________________________ > mlpack mailing list > [email protected] > http://knife.lugatgt.org/cgi-bin/mailman/listinfo/mlpack > -- Regards, Tharindu Mathew http://tharindumathew.com
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