Correlation-based Data Representation

Authors Marc Strickert, Udo Seiffert

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Marc Strickert
Udo Seiffert

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Marc Strickert and Udo Seiffert. Correlation-based Data Representation. In Similarity-based Clustering and its Application to Medicine and Biology. Dagstuhl Seminar Proceedings, Volume 7131, pp. 1-16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2007)


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.
  • Correlation
  • data representation
  • gradient-based optimization
  • clustering
  • neural gas


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