On p-adic classification

A p-adic modification of the split-LBG classification method is presented in which first clusterings and then cluster centers are computed which locally minimize an energy function. The outcome for a fixed dataset is independent of the prime number p with finitely many exceptions. The methods are applied to the construction of p-adic classifiers in the context of learning.