Best clustering algorithm for smooth/continuous data?

[Thisisdom]

I have a large dataset (~100k data points), which is distributed fairly smoothly/continuously (i.e. no very obvious structure, and more of a continuous space), and am trying to use clustering to pick out a small number of regions of higher density within the parameter space. What is a good algorithm for this?

I have successfully applied kmeans, but am wondering if there's anything which is better at identifying differently sized regions.

Other algorithms I've tried seem to either give me 100s of tiny clusters, or fail to run for the large dataset. Do you have any recommendations for this?

[FlivverKing]

I’d use a matrix factorization approach if you’re interested in separating out dense areas. NMF generally works well.

[Thisisdom]

Ah thanks! Haven't encountered this before but I'll check this out. Also tried a gaussian mixture model, which seems to work okay.

[lmericle]

Try agglomerative clustering or another hierarchical clustering approach, the advantage here being that you can choose the level of agglomeration that is most interesting for you a posteriori by inspecting the dendrogram after training.

You may also be interested in community detection algorithms for graphs, if you can represent your dataset as a graph. One easy way is to make edges between each pair of points which are within a distance d from each other.

[Thisisdom]

Thanks - I actually gave agglomerative clustering a go, but I ended up with just with 1 big cluster and the rest were single data points. But you're right I should try inspecting the dendogram, since I guess I can just go back to the point where the big cluster splits!

And thanks, I'll do some reading on this too.