TY - GEN A1 - Weber, Marcus A1 - Fackeldey, Konstantin T1 - G-PCCA: Spectral Clustering for Non-reversible Markov Chains N2 - Spectral clustering methods are based on solving eigenvalue problems for the identification of clusters, e.g., the identification of metastable subsets of a Markov chain. Usually, real-valued eigenvectors are mandatory for this type of algorithms. The Perron Cluster Analysis (PCCA+) is a well-known spectral clustering method of Markov chains. It is applicable for reversible Markov chains, because reversibility implies a real-valued spectrum. We extend this spectral clustering method also to non-reversible Markov chains and give some illustrative examples. The main idea is to replace the eigenvalue problem by a real-valued Schur decomposition. By this extension, non-reversible Markov chains can be analyzed. Furthermore, the chains need not have a positive stationary distribution. And additionally to metastabilities, dominant cycles and sinks can be identified, too. T3 - ZIB-Report - 15-35 Y1 - 2015 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/5550 UR - https://nbn-resolving.org/urn:nbn:de:0297-zib-55505 ER -