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Correlation-based Data Representation

Authors: Marc Strickert and Udo Seiffert

Published in: Dagstuhl Seminar Proceedings, Volume 7131, Similarity-based Clustering and its Application to Medicine and Biology (2007)


Abstract
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.

Cite as

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)


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@InProceedings{strickert_et_al:DagSemProc.07131.4,
  author =	{Strickert, Marc and Seiffert, Udo},
  title =	{{Correlation-based Data Representation}},
  booktitle =	{Similarity-based Clustering and its Application to Medicine and Biology},
  pages =	{1--16},
  series =	{Dagstuhl Seminar Proceedings (DagSemProc)},
  ISSN =	{1862-4405},
  year =	{2007},
  volume =	{7131},
  editor =	{Michael Biehl and Barbara Hammer and Michel Verleysen and Thomas Villmann},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/DagSemProc.07131.4},
  URN =		{urn:nbn:de:0030-drops-11347},
  doi =		{10.4230/DagSemProc.07131.4},
  annote =	{Keywords: Correlation, data representation, gradient-based optimization, clustering, neural gas}
}
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