Mining decision rules from deterministic finite automata

This work presents a novel approach for knowledge discovery from sequential data. Instead of mining the examples in their sequential form, we suppose they have been processed by a machine learning algorithm that has generalized them into a deterministic finite automaton (DFA). Thus, we present a theoretical framework to extract decision rules from this DFA. Our method relies on statistical inference theory and contrary to usual support-based frequent pattern mining techniques. It does not depend on such a global threshold, but rather allows us to determine an adaptive relevance threshold. Various experiments show the advantage of mining DFA instead of mining sequences.