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Given your initial thoughts I'd take a look at scipy.cluster.hierarchy to start with (or the equivalent in scikit-learn) to build the clusters based on distance. For instance, given a numpy.array of coordinates - coords you could build a cluster based on distance like so: import numpy as np import scipy.cluster.hierarchy import collections labels = ...


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Yes, there are memory limits and processing in chunks is faster. It's hard to say how big chunks you should use. Apart from processing in smaller parts you should also consider following: Make sure you have proper indices on your tables Increase shared_buffers and work_mem Change the function. If you're using this function there's much room for ...



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