Abstract
Agent-based models informed by empirical data are growing in popularity. Many models make extensive use of collected data for the development, initialisation or validation. In parallel, models are growing in size and complexity, generating large amounts of output data. On the other hand, Data Mining is used to extract hidden patterns from large collections of data using different techniques. This work proposes the intense use of Data Mining techniques for the improvement and development of agent-based models. It presents a methodological approach explaining why and when to use Data Mining, with a formal description of each stage of the corresponding process. This is illustrated with a case study, showing the application of the proposed approach step by step.
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Arroyo, J., Hassan, S., Gutiérrez, C. et al. Re-thinking simulation: a methodological approach for the application of data mining in agent-based modelling. Comput Math Organ Theory 16, 416–435 (2010). https://doi.org/10.1007/s10588-010-9078-y
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DOI: https://doi.org/10.1007/s10588-010-9078-y