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Similarity and Dissimilarity

  • F. Esposito
  • D. Malerba
  • V. Tamma
  • H. H. Bock
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Several classical or symbolic data analysis techniques start from the assumption that there are some means for assessing and quantifying the similarities (or dissimilarities) which may exist between the underlying objects (individuals, classes, symbolic objects, etc.), by a recourse to the observed data matrix. They use these similarities as their data input. For example, in cluster analysis where we look for ‘homogeneous’ classes C1, C2,… of objects, it is typically required that pairs of objects from the saine class have a large similarity (i.e., a small dissimilarity) and, conversely, that the similarity is small for pairs of objects fromdifferent classes (see Section 11.1).

Keywords

Aggregation Function Dissimilarity Measure Symbolic Data Logical Dependence Symbolic Object 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • F. Esposito
    • 1
  • D. Malerba
    • 1
  • V. Tamma
    • 1
  • H. H. Bock
    • 2
  1. 1.Dipartimento di InformaticaUniversità di BariGermany
  2. 2.Institut für StatistikRWTH AachenGermany

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