This article presents an approach to discover association rules that are most relevant with respect to the maximum entropy methods. Because entropy is related to information, this approach can be considered as aiming to find the most informative rules in data. The basic idea is to exploit numerical relationships that are observed by comparing (relative) frequencies, or ratios of frequencies, and so forth, as manifestations of interactions of underlying conditional knowledge.
My approach differs from usual knowledge discovery and data-mining methods in various respects:
It explicitly takes the instrument of inductive inference into consideration.
It is based on statistical information but not on probabilities close to 1; actually, it mostly uses only structural information obtained from the data.
It is not based on observing conditional independencies (as for learning causal structures), but aims at learning relevant conditional dependencies in a nonheuristic way.
As a further novelty, it does not compute single, isolated rules, but yields a set of rules by taking into account highly complex interactions of rules.
Zero probabilities computed from data are interpreted as missing information, not as certain knowledge.