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000049823 084__ $$2WoS$$aPhysics, Multidisciplinary
000049823 1001_ $$0P:(DE-Juel1)VDB46297$$aKraskov, A.$$b0$$uFZJ
000049823 245__ $$aHierarchical clustering using mutual information
000049823 260__ $$aLes Ulis$$bEDP Sciences$$c2005
000049823 300__ $$a278 - 284
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000049823 440_0 $$01996$$aEurophysics Letters$$v70$$x0295-5075$$y2
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000049823 520__ $$aWe present a conceptually simple method for hierarchical clustering of data called mutual information clustering ( MIC) algorithm. It uses mutual information (MI) as a similarity measure and exploits its grouping property: The MI between three objects X, Y, and Z is equal to the sum of the MI between X and Y, plus the MI between Z and the combined object (XY). We use this both in the Shannon (probabilistic) version of information theory and in the Kolmogorov ( algorithmic) version. We apply our method to the construction of phylogenetic trees from mitochondrial DNA sequences and to the output of independent components analysis (ICA) as illustrated with the ECG of a pregnant woman.
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000049823 7001_ $$0P:(DE-Juel1)VDB53716$$aStoegbauer, H.$$b1$$uFZJ
000049823 7001_ $$0P:(DE-Juel1)VDB48529$$aAndrzejak, R. G.$$b2$$uFZJ
000049823 7001_ $$0P:(DE-Juel1)136887$$aGrassberger, P.$$b3$$uFZJ
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000049823 8567_ $$uhttp://dx.doi.org/10.1209/epl/i2004-10483-y
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000049823 9141_ $$y2005
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000049823 9201_ $$0I:(DE-Juel1)NIC-20090406$$gNIC$$kNIC$$lJohn von Neumann - Institut für Computing$$x0
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