Improving Data Explainability in Analysis of Designed Computer Simulation Experiments

Shengkun Xie, Anna T. Lawniczak, Junlin Hao, Chong Gan

Abstract: Dimension reduction of data generated from a complex simulation model is an important aspect, for the purpose of better understanding the behaviour of data, and it is often needed in many fields of study, including computer simulation and modelling. Also, improving data explainability is highly desirable for studying dynamics of complex simulation models, dynamics of which depends on many parameters, and has become an important aspect in machine learning and artificial intelligence. In this work, we initiate an approach, combining principal component analysis, K-means clustering and ANOVA-F test, in order to analyze the data from a designed simulation experiment. We propose a new method for optimal selection of numbers of clusters for data clustering. The proposed method is illustrated by an analysis of agent-based computer simulation. Our study has demonstrated the usefulness of the proposed method in both explainable data analytic and analysis of complex systems.

External IDs:dblp:conf/ssci/XieLHG19