Finding metastable sets as dominant structures of Markov processes has been shown to be especially useful in modeling interesting slow dynamics of various real world complex processes. Furthermore, coarse graining of such processes based on their dominant structures leads to better understanding and dimension reduction of observed systems. However, in many cases, e.g., for nonreversible Markov processes, dominant structures are often not formed by metastable sets but by important cycles or a mixture of both. This paper aims at understanding and identifying these different types of dominant structures for reversible as well as nonreversible ergodic Markov processes. Our algorithmic approach generalizes spectral-based methods for reversible processes by using Schur decomposition techniques which can also tackle nonreversible cases. We illustrate the mathematical construction of our new approach by numerical experiments.