2003/11/27 by Alexander Kraskov, Kraskov, Alexander, Harald Stoegbauer +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Physics and Astronomy · #Blind Source Separation Techniques #Computational Complexity (cs.CC) #Data Analysis #FOS: Biological sciences #FOS: Computer and information sciences #FOS: Physical sciences #Fractal and DNA sequence analysis #Machine Learning in Bioinformatics #Quantitative Methods (q-bio.QM) #Statistics and Probability (physics.data-an) #cs.CC #physics.data-an #q-bio.QM
paper · pdf · doi:10.48550/arxiv.q-bio/0311037
4 pages, 4 figures
arxiv created 2003/11/27 · openalex publication_date 2003/11/27 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We present a method for hierarchical clustering of data called \it 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.