---
res:
  bibo_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.\r\n@eng"
  bibo_authorlist:
  - foaf_Person:
      foaf_givenName: Andreas
      foaf_name: Müller, Andreas
      foaf_surname: Müller
  - foaf_Person:
      foaf_givenName: Sebastian
      foaf_name: Nowozin, Sebastian
      foaf_surname: Nowozin
  - foaf_Person:
      foaf_givenName: Christoph
      foaf_name: Lampert, Christoph
      foaf_surname: Lampert
      foaf_workInfoHomepage: http://www.librecat.org/personId=40C20FD2-F248-11E8-B48F-1D18A9856A87
    orcid: 0000-0001-8622-7887
  bibo_doi: 10.1007/978-3-642-32717-9_21
  bibo_volume: 7476
  dct_date: 2012^xs_gYear
  dct_language: eng
  dct_publisher: Springer@
  dct_title: Information theoretic clustering using minimal spanning trees@
...