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