PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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 mining sequences.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:386
Deposited By:François Jacquenet
Deposited On:18 December 2004