Abstract
The research is carried out in the area of analysis of simulation results by using data mining techniques. The goal of the research is to explore the applicability of data mining techniques in the area of simulation results analysis, to offer an application scheme of data mining techniques in the analysis of simulation results, as well as to demonstrate the usage of these techniques in the analysis of experimental data. As a result of the theoretical study, an approach is proposed, consisting of two stages and combining the fundamental techniques of data farming and knowledge discovery. A variety of data mining techniques, such as correlation analysis, clustering and several visualization mechanisms of results, are used for knowledge discovery. The proposed approach is applied to the analysis of experimental data. The performance of a queueing system is analysed, and knowledge and decision rules are obtained from simulation results.
Highlights
Simulation and data mining are two technologies; each helps solve different problems in various areas of life
The discussion is limited to the results obtained from a discrete-event system (DES) simulation models, which are most effectively used for the analysis of dynamics of complex artificial material systems
Data mining is described as the process, during which previously unknown, non-trivial, practically useful and accessible knowledge is obtained from the raw data [11]
Summary
Simulation and data mining are two technologies; each helps solve different problems in various areas of life. The traditional approach to simulation output analysis implies replication design, estimation of performance indicators and analysis of system behaviour, based on these estimations [4] This final analysis cannot be reduced to statistical methods only. Data mining is described as the process, during which previously unknown, non-trivial, practically useful and accessible knowledge is obtained from the raw data [11] This technology is capable of finding significant linkages in big data arrays and identify behavioural patterns in order to help people make reasonable decisions. As a result of data mining techniques application in simulation results analysis, the knowledge is obtained, which increases the simulation efficiency. The authors provide a review of approaches in the area of analysis of simulation results by using data mining techniques.
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