TY - JOUR A1 - Frank, Anna-Simone A1 - Sikorski, Alexander A1 - Röblitz, Susanna T1 - Spectral clustering of Markov chain transition matrices with complex eigenvalues T2 - Journal of Computational and Applied Mathematics N2 - The Robust Perron Cluster Analysis (PCCA+) has become a popular spectral clustering algorithm for coarse-graining transition matrices of nearly decomposable Markov chains with transition states. Originally developed for reversible Markov chains, the algorithm only worked for transition matrices with real eigenvalues. In this paper, we therefore extend the theoretical framework of PCCA+ to Markov chains with a complex eigen-decomposition. We show that by replacing a complex conjugate pair of eigenvectors by their real and imaginary components, a real representation of the same subspace is obtained, which is suitable for the cluster analysis. We show that our approach leads to the same results as the generalized PCCA+ (GPCCA), which replaces the complex eigen-decomposition by a conceptually more difficult real Schur decomposition. We apply the method on non-reversible Markov chains, including circular chains, and demonstrate its efficiency compared to GPCCA. The experiments are performed in the Matlab programming language and codes are provided. Y1 - 2024 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/8758 VL - 444 SP - 115791 ER -