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 minimise an energy function. The outcome for a fixed dataset is independent of the prime number $p$ with finitely many exceptions. In the end, the methods are applied to the construction of $p$-adic classifiers in the context of learning.