Information theoretic clustering using minimal spanning trees LNCS Müller, Andreas Nowozin, Sebastian Lampert, Christoph ; https://orcid.org/0000-0001-8622-7887 In this work we propose a new information-theoretic clustering algorithm that infers cluster memberships by direct optimization of a non-parametric mutual information estimate between data distribution and cluster assignment. Although the optimization objective has a solid theoretical foundation it is hard to optimize. We propose an approximate optimization formulation that leads to an efficient algorithm with low runtime complexity. The algorithm has a single free parameter, the number of clusters to find. We demonstrate superior performance on several synthetic and real datasets. Springer 2012 info:eu-repo/semantics/conferenceObject doc-type:conferenceObject text http://purl.org/coar/resource_type/c_5794 https://research-explorer.ista.ac.at/record/3126 Müller A, Nowozin S, Lampert C. Information theoretic clustering using minimal spanning trees. In: Vol 7476. Springer; 2012:205-215. doi:<a href="https://doi.org/10.1007/978-3-642-32717-9_21">10.1007/978-3-642-32717-9_21</a> eng info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-32717-9_21 info:eu-repo/semantics/closedAccess