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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},
}