Decision Theoretic Fusion Framework for Actionability Using Data Mining on an Embedded System - Data Mining: Theory, Methodology, Techniques, and Applications - page 103

combining rules from interface modalities, data fusion rules, DBMS processing, and
communication tasks are done by the speech interactive agent. In this system, a con-
versational tool provides advantages in confirming the final decision to use human
interactive data mining [3].
5.2 Conclusions
In this paper, we proposed a decision theoretic fusion framework that includes the
multiple level-of-abstraction approach combining multiple-level association rules and
the summary table, as well as the active interaction rule generation algorithm using
the rough set theory for actionability on an embedded car navigation system. In addi-
tion, it included the sensory and data fusion level rule extraction algorithm to cope
with simultaneous events occurring from multi-modal interface. Using such a deci-
sion theoretic fusion framework, a variety of applications can be applied easily to this
system in the form of flexible, extensible and transparent ones. We expect that this
fusion framework will be able to meet the user's demands and desires.
Acknowledgements
This work was supported by grant No. A17-11-02 from the Korea Institute of Indus-
trial Technology Evaluation & Planning Foundation.
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