I have a set of curves in 2D space each expressed as a set of (sampled) data points. Each set has more or less the same number of items - eventually I guess I’ll use binning to make sure the number of points is the same (say 50) if that can help.
I would like to cluster the curves in N groups. Computing N should be part of the solution.
Possible translations on the first dimension are irrelevant.
I have a k-means implementation available.
I was thinking to transform the problem into a 100-dimensional space (50x2) where the samples of each curve become features.
Could this approach work? Is there a better one, either using k-means or a different algorithm?