Given the urgency to reduce greenhouse gas emissions in the whole economy, the abatement of nitrous oxide (N₂O) built-up in biological wastewater treatment would be an important contribution of the waste sector. However, the complexity of N₂O-formation in activated sludge and non-linear dynamics of operating factors pose difficulties to apply effective measures for a specific high-emission situation in a full-scale context. Facing such complex interactions and unknown relationships, data mining can provide useful support to analyze full-scale datasets. Therefore, the goal of this article is to investigate a data-driven method to understand high-emission patterns and their origins to provide a basis for the development of N₂O-mitigation measures. We applied unsupervised artificial neural networks (self-organizing maps) and subsequent clustering to a 3-year, high-resolution dataset to identify and characterize operational states and to analyze the transitions among them. In the case study, hampered denitrification, anaerobic digestion supernatant addition, and indications of snowmelt were found among problematic situations. The transition analysis showed the importance of contextualizing a high-emission pattern as it can emerge from different origins, having implications when developing mitigation measures. Apart from analyzing the shift of operational states, a key advantage of the proposed methodology is the consideration of the combined effect of variables in specific situations. This renders it an effective tool to understand operational patterns. It can further be used to inform experiments by formulating hypotheses and prioritizing variables combinations; providing finally insights towards the development of situation-specific strategies for a low-N₂O-operation of full-scale plants.