  Motivation: Clustering is a frequently used concept in variety of
bioinformatical applications. We present a new 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).
  Results: We use this both in the Shannon (probabilistic) version of
information theory, where the "objects" are probability distributions
represented by random samples, and in the Kolmogorov (algorithmic) version,
where the "objects" are symbol sequences. We apply our method to the
construction of mammal phylogenetic trees from mitochondrial DNA sequences and
we reconstruct the fetal ECG from the output of independent components analysis
(ICA) applied to the ECG of a pregnant woman.
  Availability: The programs for estimation of MI and for clustering
(probabilistic version) are available at
http://www.fz-juelich.de/nic/cs/software

Quantitative Methods;Computational Complexity;Biological Physics