Pattern recognition is becoming an increasingly important tool for making inferences from the massive amounts of data produced in fusion experiments. The purpose is to contribute to physics studies and plasma control. In this work, we address the visualization of plasma confinement data, the (real-time) identification of confinement regimes and the establishment of a scaling law for the energy confinement time. We take an intrinsically probabilistic approach, modeling data from the International Global H-mode Confinement Database with Gaussian distributions. We show that pattern recognition operations working in the associated probability space are considerably more powerful than their counterparts in a Euclidean data space. This opens up new possibilities for analyzing confinement data and for fusion data processing in general. We hence advocate the essential role played by measurement uncertainty for data interpretation in fusion experiments.