  A $p$-adic modification of the split-LBG classification method is presented
in which first clusterings and then cluster centers are computed which locally
minimise 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.
