Association rule learning for multi-classification suggestions The task is the following: given a training set of medical symptoms and an associated diagnosis, output a list of the most likely diagnosises for a combination of symptoms.
As of now, a solution exists which makes use of association rule learning methods: we find rules on the attributes of the training dataset and infer the likely diagnosises and their probability from the confidence we have in said rules to hold true.
However, this method doesn't seem to be able to scale for large datasets because of the sheer number of different attributes ($10^4$) and possible classes ($10^3$). Hence my question: are association rules a viable solution for this problem? Are there any alternatives?
Assuming there are many repetitions of each attribute and class, an embedding space can be learned. Attributes and classes that co-occur together will be projected into nearby space. One example is affinity weighted embedding.

Then prediction becomes approximate nearest neighbor search. For a given set of attributes, find the nearest class(es). One example is locality-sensitive hashing.

The strategy of combining learned embedding spaces and approximate nearest neighbor search scales very well for prediction. Search time can remain constant if you are willing to accept a looser definition of "approximate" as the data grows.