I am using python/numpy to create a histogram as follows:
points_for_histogram = list() # fill list with data ... unique_pts = set(points_for_histogram) hist, bins = np.histogram(points_for_histogram, bins=len(unique_pts))
The generated histogram looks something like this:
So clearly there are these 5 clusters in the histogram. What would be a good way to find the centroid of these clusters? I was thinking to find where the signal drops to zero and try to partition the histogram like that but I think this might not be robust enough.
Is there some other clustering method I could try? I know of K-means but this needs to know the number of clusters a priori.