This yields an LVC parametrization to define a sum-of-products Hamiltonian suitable for ML-MCTDH. This Hamiltonian is used to perform TDH simulations which provides a correlation matrix of coordinate expectation values. The related adjacency matrix serves as input for a spectral clustering algorithm yielding ML-trees whose structure can be influenced, e.g. by setting thresholds for the adjacency matrix. Different ML-trees may generally result in vastly different efforts for obtaining convergence with respect to the number of SPFs entering the wave packet expansion.