@inproceedings{3126,
  abstract     = {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.
},
  author       = {Müller, Andreas and Nowozin, Sebastian and Lampert, Christoph},
  location     = {Graz, Austria},
  pages        = {205 -- 215},
  publisher    = {Springer},
  title        = {{Information theoretic clustering using minimal spanning trees}},
  doi          = {10.1007/978-3-642-32717-9_21},
  volume       = {7476},
  year         = {2012},
}