2007/01/01 by Marc Strickert, Strickert, Marc, Udo Seiffert +1 · 5 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · #Advanced Clustering Algorithms Research #Artificial intelligence #Bioinformatics and Genomic Networks #Cluster analysis #Computer science #Correlation #Data mining #Data science #Data visualization #External Data Representation #Focus (optics) #Frontier #Function (biology) #Gene expression and cancer classification #Geography #Image (mathematics) #Machine learning #Mathematics #Physics #Representation (politics) #Similarity (geometry) #Visualization #clustering #data representation #gradient-based optimization #neural gas
paper · pdf · doi:10.4230/dagsemproc.07131.4
published in DROPS (Schloss Dagstuhl – Leibniz Center for Informatics), 0 (Schloss Dagstuhl – Leibniz Center for Informatics)
openalex publication_date 2007/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
The Dagstuhl Seminar 'Similarity-based Clustering and its Application to Medicine and Biology' (07131) held in March 25--30, 2007, provided an excellent atmosphere for in-depth discussions about the research frontier of computational methods for relevant applications of biomedical clustering and beyond. We address some highlighted issues about correlation-based data analysis in this seminar postribution. First, some prominent correlation measures are briefly revisited. Then, a focus is put on Pearson correlation, because of its widespread use in biomedical sciences and because of its analytic accessibility. A connection to Euclidean distance of z-score transformed data outlined. Cost function optimization of correlation-based data representation is discussed for which, finally, applications to visualization and clustering of gene expression data are given.