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Hierarchical clustering using mutual information

TitleHierarchical clustering using mutual information
Publication TypeJournal Article
AuthorsKraskov A, Stogbauer H, Andrzejak RG, Grassberger P
Date PublishedAPR

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.