Integration of Complex System Simulation and Data Mining

Abstract

Simulation is a common method for studying the behavior of complex systems and revealing the mechanism of the system. However, complex systems have many parameters, non-linear interactions, and complex evolutionary dynamics. It is difficult to reveal the mechanism of complex systems. Especially complex system simulation experiments may produce a large amount of data. How to summarize the macroscopic mode of the system, identify key factors, and discover the relationship between input and output variables, still lacks an effective method. This paper proposes an integrated framework for simulation modeling and data mining, which combines data mining and simulation modeling to conduct iterative experimental exploration and analysis of complex systems. Data mining techniques were used in multiple stages of modeling and simulation, including: ETL on raw data, text mining and process mining to build conceptual models, uniform experimental design to generate simulation data, and clustering of simulation data to identify system macro patterns, use stepwise regression, neural network, etc. to build a meta-model of a complex system. The introduction of data mining can improve the ability and efficiency of complex system modeling and simulation.