Unsupervised learning can detect multivariate anomalies in regional data that traditional single-variable checks miss, helping statistical agencies distinguish between data quality issues and genuine structural divergence.
This paper uses five unsupervised machine learning techniques to detect regions in Europe with unusual combinations of economic and social indicators, rather than just extreme individual values.