  The aim of this paper is to investigate the rebinding effect, a phenomenon
describing a "short-time memory" which can occur when projecting a Markov
process onto a smaller state space. For guaranteeing a correct mapping by the
Markov State Model, we assume a fuzzy clustering in terms of membership
functions, assigning degrees of membership to each state. The macro states are
represented by the membership functions and may be overlapping. The magnitude
of this overlap is a measure for the strength of the rebinding effect, caused
by the projection and stabilizing the system. A minimal bound for the rebinding
effect included in a given system is computed as the solution of an
optimization problem. Based on membership functions chosen as a linear
combination of Schur vectors, this generalized approach includes reversible as
well as non-reversible processes.
