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Mining Decision Rules from Deterministic Finite Automata AbstractThis paper 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 statsitical 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 adaptative relevance threshold. Various experiments show the advantage of mining DFA instead of mining sequences.
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