How to calculate a weighted Hierarchical clustering in Orange I am doing my first cluster analysis with Orange (which I recently discovered and looks promising for this iterative and interactive process). 

Apparently, there are several methods of creating clusters based on distance algorithm:


Single linkage (which computes the distance between the closest elements of the two clusters)
Average linkage (which computes the average distance between elements of the two clusters)
Complete linkage (which computes the distance between the clusters’ most distant elements)
Weighted linkage 
Ward


Since I have several columns, and some of them are more important than others in terms of defining clusters, it seems to me that using weighted linkage method may be what I am looking for. Unfortunately, I don't know how to do that, since I didn't find a way to assign a weight to each column.

To make things worse, I have only found an explanation about the first three on this Orange's blog post but nothing about Weighted linkage  (nor Ward, which may be a recent addition, since it is not even mentioned on widget's help).

Am I on the right path to achieve what I am looking for? Is there any way to make some columns more or less important/definitory when calculating the distances?
Weighted linkage probably does not mean you get to specify weights of features (build the distance matrix yourself!)

Instead this most likely refers to the well-known weighted group average strategy you will find in most textbooks often called WPGMA. There are two different definitions of "average", so this is likely simply the "other" average linkage.