Mining Decision Rules from Deterministic Finite Automata
François Jacquenet, Marc Sebban and Georges Valetudie
In: ICTAI 2004, 15-17 Nov 2004, Boca Raton, Florida, USA.
This 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
|Postscript - PASCAL Members only - Requires a viewer, such as GhostView|