Hi Ted, Thanks for the reply.
I have been using DBSCAN (in python), the one implemented in sci-kit package. For a dataset with about 8k points, the running time on my Intel i7 4700 QM comes to around ~300 seconds. I have implemented a parallel version using the multiprocessing python library, and the running time comes down to about 100~120 seconds, when I 3 parallel threads. Thus the scale up is almost 'n'. I think scalability should not be an issue for a Map Reduce implementation. Chirag Nagpal University of Pune, India www.chiragnagpal.com ________________________________________ From: Ted Dunning <ted.dunn...@gmail.com> Sent: Sunday, November 30, 2014 6:29 PM To: user@mahout.apache.org Subject: Re: DBSCAN implementation in Mahout On Sat, Nov 29, 2014 at 8:31 PM, 3316 Chirag Nagpal < chiragnagpal_12...@aitpune.edu.in> wrote: > Since Density based clustering algorithms, are being utilised extensively, > especially by the GIS research groups, it is a bit sad that there isn't a > Map Reduce implementation available.. > > I think I will propose to write MapReduce code for DBSCAN and OPTICS for > GSoC '15. > > I would like to take your input as to how much of significance would this > be of to the community in general? > We have had proposals to add this to Mahout, but as far as I remember, no credible requests to use it. Also, there is the question of scalability of dbscan like algorithms.
