Non-ordinal representation can be applied using various techniques such as multi-criteria decision analysis (MCDA), principal component analysis (PCA), and machine learning algorithms. These methods help to visualize and interpret complex datasets, highlighting the trade-offs and synergies between different catalytic properties. For instance, PCA can reduce the dimensionality of a dataset while preserving the variability, offering insights into which factors are most influential.
