Sample orientation in the class, clustered by k-means in Python
The way you're defining the orientation to us seems like you've got the right idea. If you use the farthest distance from the center as the denominator, then you'll get 0 as your minimum (cluster center) and 1 as your maximum (the farthest distance) and a linear distance in-between.
I've got some clustered classes, and a sample with a prediction. Now, i want to know the "orientation" of the sample, which varies from 0 to 1, where 0 - right in the class center, 1 - right on the class border(radius). I guess, it's going to be
orientation=dist_from_center/class_radius
So, I'm struggled to find class radius. The first idea is to take the distance from a center to the most distant sample, but iwould like to use smth more 'academic' and less custom