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@ARTICLE{Kraskov:49823,
      author       = {Kraskov, A. and Stoegbauer, H. and Andrzejak, R. G. and
                      Grassberger, P.},
      title        = {{H}ierarchical clustering using mutual information},
      journal      = {epl},
      volume       = {70},
      issn         = {0295-5075},
      address      = {Les Ulis},
      publisher    = {EDP Sciences},
      reportid     = {PreJuSER-49823},
      pages        = {278 - 284},
      year         = {2005},
      note         = {Record converted from VDB: 12.11.2012},
      abstract     = {We present a conceptually simple method for hierarchical
                      clustering of data called mutual information clustering (
                      MIC) algorithm. It uses mutual information (MI) as a
                      similarity measure and exploits its grouping property: The
                      MI between three objects X, Y, and Z is equal to the sum of
                      the MI between X and Y, plus the MI between Z and the
                      combined object (XY). We use this both in the Shannon
                      (probabilistic) version of information theory and in the
                      Kolmogorov ( algorithmic) version. We apply our method to
                      the construction of phylogenetic trees from mitochondrial
                      DNA sequences and to the output of independent components
                      analysis (ICA) as illustrated with the ECG of a pregnant
                      woman.},
      keywords     = {J (WoSType)},
      cin          = {NIC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)NIC-20090406},
      pnm          = {Betrieb und Weiterentwicklung des Höchstleistungsrechners},
      pid          = {G:(DE-Juel1)FUEK254},
      shelfmark    = {Physics, Multidisciplinary},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000228627600020},
      doi          = {10.1209/epl/i2004-10483-y},
      url          = {https://juser.fz-juelich.de/record/49823},
}