  We investigate machine learning based on clustering techniques that are
suitable for the detection of encoded strings of q-ary symbols transmitted over
a noisy channel with partially unknown characteristics. We consider the
detection of the q-ary data as a classification problem, where objects are
recognized from a corrupted vector, which is obtained by an unknown corruption
process. We first evaluate the error performance of k- means clustering
technique without constrained coding. Secondly, we apply constrained codes that
create an environment that improves the detection reliability and it allows a
wider range of channel uncertainties.
