Abstract—Researchers take years and even decades of
observation in order to analyze socio-economic phenomenon.
Whereas the agent-based modeling simulation (ABMS)
provides a new issue by offering the possibility to create virtual
societies in which individuals and organizations are directly
represented with their observed interactions. As it is known
simulation generates and consumes a large amount of data. The
analysis of these data which may contain implicit and hidden
information will always remain a very difficult phase in the
ABMS. As a solution to this problematic, the use of data mining
techniques can contribute to the right analysis of the
phenomena that emerges in these systems. In this paper we aim
the investigation of agent-based modeling simulation and data
mining techniques.
Index Terms—Agent-based modeling and simulation, data
mining, analysis behaviors, emergence.
The authors are with the Department of Computer Science, University of
Bordj Bou Arreridj, Algeria (e-mail: m.saadsaoud@univ-bba.dz,
a.boubetra@univ-bba.dz, s.attia@univ-bba.dz).
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Cite:Manel Saad Saoud, Abdelhak Boubetra, and Safa Attia, "How Data Mining Techniques Can Improve Simulation Studies," International Journal of Computer Theory and Engineering vol. 6, no. 1, pp. 15-19, 2014.
