Stochastic simulation
Understanding Anasazi culture change through agent-based modeling
Dynamics in human and primate societies
The use of models—making MABS more informative
MABS 2000 Proceedings of the second international workshop on Multi-agent based simulation
Mining the network value of customers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Simulation for the Social Scientist
Simulation for the Social Scientist
Multiagent Coordination with Learning Classifier Systems
IJCAI '95 Proceedings of the Workshop on Adaption and Learning in Multi-Agent Systems
Quality assessment, verification, and validation of modeling and simulation applications
WSC '04 Proceedings of the 36th conference on Winter simulation
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Modeling spread of ideas in online social networks
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Verification and validation of simulation models
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come
AIMSS: An Architecture for Data Driven Simulations in the Social Sciences
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Deepening the Demographic Mechanisms in a Data-Driven Social Simulation of Moral Values Evolution
Multi-Agent-Based Simulation IX
A data-driven simulation of social values evolution
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Mentat: a data-driven agent-based simulation of social values evolution
MABS'09 Proceedings of the 10th international conference on Multi-agent-based simulation
A case study in model selection for policy engineering: simulating maritime customs
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
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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.