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More accurately, for VCS, I would guess attributes don't have a level of "importance" (in the algorithm itself, using some is more important than using others), you just test how much you can segment the data while still having enough data, and choose arbitrary cutoff points, which is probably why I find KNN more intuitive. But I'm still trying to think of a proof for an optimal similarity between a VCS system with evenly distributed data accross all dimensions, with a set number of divisions in each dimension, but the divisions being started arbitrarily while being evenly spaced (ie. any number between 0 and 100 is the start of the first distance bin, each bin covers 100 units, and we pretend the points can't be less that the minimum value)