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Correlation-based Data Representation

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CONTRIBUTORS:
  Author Marc Strickert
  Author Udo Seiffert
PROCEEDINGS TITLE:
  Similarity-based Clustering and its Application to Medicine and Biology
YEAR: 2007
PUB TYPE: Conference Paper in Proceedings
PAGES: 1 - 16
SUBJECT(S): correlation; data representation; gradient-based optimization; clustering; neural gas; visualization; multi-dimensional scaling
DISCIPLINE: Mathematics
HTTP: http://lori.academicdirect.org/cites/200705.pdf
LANGUAGE: English
PUB ID: 103-437-772 (Last edited on 2009/11/09 07:28:58 US/Mountain)
SPONSOR(S):
 
ABSTRACT:
The Dagstuhl Seminar Similarity-based Clustering and its
Application to Medicine and Biology (07131) held in March 2530, 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.
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