This is a nice application of the idea that understanding and compression are equivalent, which is the inspiration behind the Hutter prize (500 K€ for whoever can compress Wikipedia best, see http://prize.hutter1.net/ - the current state of the art is about 15 MB).

Compression can also be used as "machine learning method": but all data vectors with the same class label into a separate bucket; any new, unseen and unlabeled data item can be added to each bucket. The most likely class is the one the bucket of which grows the least when compressed after adding it. The University of Waikato group (Ian Witten and co-workers) did a fair amount of that kind of work, perhaps first, e.g. Frank, Eibe, Chang Chui and Ian H. Witten (2000) "Text categorization using compression models", https://www.cs.waikato.ac.nz/~eibe/pubs/Frank_categorization... - yes, published 23 years ago!).