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 standardized form of Euclidean distance which standardized data with adjustments made for correlation between variables and weights all variables equally. Why this distance measure is frequently used in GM cluster analysis?? Thanks in advance Elahe -- MORPHMET may be accessed via its webpage at http://www.morphometrics.org --- You received this message because you are subscribed to the Google Groups "MORPHMET" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected].
