  This note proposes a simple and general framework of dynamic mode
decomposition (DMD) and a mode selection for large datasets. The proposed
framework explicitly introduces a preconditioning step using an incremental
proper orthogonal decomposition to DMD and mode selection algorithms. By
performing the preconditioning step, the DMD and the mode selection can be
performed with low memory consumption and small computational complexity and
can be applied to large datasets. In addition, a simple mode selection
algorithm based on a greedy method is proposed. The proposed framework is
applied to the analysis of a three-dimensional flows around a circular
cylinder.
