I'm looking to properly cluster a dataset of roughly 3 million data points in 3-space. The shapes form closely spaced "clusters" that resemble pancakes.
Here is a downsampled dataset:
data = Import["https://dl.dropbox.com/u/28603777/data.wdx"];
I have tried the standard distance methods (Canberra, Euclidean, Manhattan and BrayCurtis), yet each leaves some points from one cluster improperly sorted into another.
out = FindClusters[data, 6];
The navy platelet "leaks" into the gold platelet due to the fact that the distance methods are isotropic. Also the gold platelet "leaks" into the red platelet.
Is there a different way to cluster such that the distance method can be optimized in this way, or is there a fast way to cluster these platelets that does not rely on this function.