In this study, machine learning is used to identify types of jumps that share motion patterns of saltating particles in sediment transport based on DNS data. For this purpose, a framework is proposed, consisting of a partitioning of jumps and a subsequent calculation of prototypical jumps. The partitioning is achieved through cluster ensembles that aggregate the results from numerous k-means clustering models. The inputs for these models consist of large numbers of features per jump, extracted via time series analysis. As a result, four different types of jumps, associated with different stages of transport, are identified. Corresponding prototypical jumps are computed using a modified variant of dynamic time warping barycenter averaging and serve as models for the types. The framework contributes to improving the understanding of particle saltation.