Hello all,
I have seen in many GM articles people use Mahalanobis distance for cluster
analysis. What is the advantage of using Mahalanobis distance over
Euclidian distance as similarity measure in cluster analysis of shape
variables?
As far as I know Mahalanobis distance is the standardize
I can not speak directly to why it is frequently used in GM cluster analysis
but I would like to mention how I look at Mahalanobis distance based on its
calculation.
Mahalanobis distance is not a pure distance metric like Euclidian or Manhattan
distance, as you have stated it is ‘standardized’.
Dear Joseph,
Thanks for your detailed explanation. As it is recommended by Claude in
"morphometrics with R" (2008) it's better to use the Mahalanobis distance
for clustering group means, because this will be scaled by the within-group
variance-covariance. In my analysis, I calculated the mean v
The distinction is that Mahalanobis distance should be thought of as a
statistical distance. For a single variable it is like a z-score (a difference
divided by a standard deviation). It is not a measure of the absolute amount of
difference. In the multivariate case Mahalanobis distance is relat
