I have to do a cluster analysis and I'm asking which distance should I used.
I know that 99% of the clustering are made using a euclidean distance, but I heard about the Mahalanobis distance and it seems to be better because it takes into account the covariance matrix of the data.
Question : Why the Mahalanobis distance isn't more used ?
For instance with this data (70% of the variance within these 2 Dim) :
The euclidean distance doesn't fit, so does the Mahalanobis distance can better fit ?
Edit : By the euclidean distance doesn't fit I mean the clusters which become apparent haven't a circle shape
Why the Mahalanobis distance isn't more used?
In most cases of clustering, using Mahalanobis in place of Euclidean is not much gain. Mahalanobis is Euclidean attuned to the ellipsoid shape of the data cloud. Ellipsoid or circular - the clusters in the cloud can be any shape and orientation. I would be nice to use Mahalanobis if one knew these characteristics (in a form of covariance matrix) for each separate cluster. But you can't know it beforehand! Sooner than the clusters are discovered. $\endgroup$ – ttnphns Dec 19 '13 at 19:53