Cluster-based reduced-order modelling (CROM) builds on the pioneering works of Gunzburger's group in cluster analysis [1] and Eckhardt's group in transition matrix models [2] and constitutes a potential alternative to reduced-order models based on a proper-orthogonal decomposition (POD). This strategy frames a time-resolved sequence of flow snapshots into a Markov model for the probabilities of cluster transitions. The information content of the Markov model is assessed with a Kullback-Leibler entropy. This entropy clearly discriminates between prediction times in which the initial conditions can be inferred by backward integration and the predictability horizon after which all information about the initial condition is lost. This approach is exemplified for a class of fluid dynamical benchmark problems like the periodic cylinder wake, the spatially evolving incompressible mixing layer, the bi-modal bluff body wake, and turbulent jet noise. For these examples, CROM is shown to distil nontrivial quasi-attr...