21–30 of 41 posts
Cool. I have not looked into the *DBSCAN methods, yet. This post makes me think I should.
I wrote an article about mean-shift a while back if anyone is interested in more details about it - https://spin.atomicobject.com/2015/05/26/mean-shift-clusteri... Some comments on K-Means - one large limitation of K-Means is that it assumes spherical shaped clusters. It will fail terribly for any other cluster shape. It's interesting that the author compared results on the same data set for the different algorithms.…
I agree that on some level more data sets would be nice, but I felt that it cluttered and obscured the exposition. Instead I used the one synthetic dataset, but crafted in to have various properties (noise, cluster shape, variable density, non-standard distributions) that will confound many different clustering approaches ... it is meant to be the "hard" case that with all the difficulties and confounding factors rol…
how about t-SNE for clustering? https://lvdmaaten.github.io/tsne/
http://scikit-learn.org/stable/modules/classes.html#module-s...
how about t-SNE for clustering? https://lvdmaaten.github.io/tsne/
That being said, I've seen the t-SNE used as a preprocessing step prior to clustering, as in ACCENSE[1].
Why not use Density Cluster based on this 2014 Science Paper? http://science.sciencemag.org/content/344/6191/1492
Is it just a matter of tweaking the definition of density / distance to the number of hops, or is it a different problem entirely? I can see how with 0 or 1 hops the data would be a very smushed distribution, versus 2d distance is much more rich and spread out.
Earlier quoted context omitted.
I agree that on some level more data sets would be nice, but I felt that it cluttered and obscured the exposition. Instead I used the one synthetic dataset, but crafted in to have various properties (noise, cluster shape, variable density, non-standard distributions) that will confound many different clustering approaches ... it is meant to be the "hard" case that with all the difficulties and confounding factors rol…
Cool, I think you did a great job. Do you have run time data for each algorithm on that data set?
These all look like clustering based on a 2d space, but does anyone know methods to tackle clustering on a network? Is it just a matter of tweaking the definition of density / distance to the number of hops, or is it a different problem entirely? I can see how with 0 or 1 hops the data would be a very smushed distribution, versus 2d distance is much more rich and spread out.
For n-dimensional space, geometric distance is often (but not always) used, but you can just use # of hops instead of that.
As someone intrested in the subject but with 0 insight or knowledge, would these algorithms be a good match for short text clustering? For example identifying identical products in a price comparison app based on their similar but not identical title/name and possibly other attributes.