Advances in IT infrastructure have enabled the generation and storage of very large data sets describing complex systems continuously in time. These can derive from both simulations and measurements. Analysis of such data requires the availability of scalable algorithms. In this contribution, we propose a scalable algorithm that partitions instantaneous observations (snapshots) of a complex system into kinetically distinct sets (termed basins). To do so, we use a combination of ordering snapshots employing the method’s only essential parameter, i.e., a definition of pairwise distance, and annotating the resultant sequence, the so-called progress index, in different ways. Specifically, we propose a combination of cut-based and structural annotations with the former responsible for the kinetic grouping and the latter for diagnostics and interpretation. The method is applied to an illustrative test case, and the scaling of an approximate version is demonstrated to be O(N log N) with N being the number of snapshots. Two real-world data sets from river hydrology measurements and protein folding simulations are then used to highlight the utility of the method in finding basins for complex systems. Both limitations and benefits of the approach are discussed along with routes for future research.