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Topographic Mapping of Dissimilarity Data

  • Barbara Hammer
  • Andrej Gisbrecht
  • Alexander Hasenfuss
  • Bassam Mokbel
  • Frank-Michael Schleif
  • Xibin Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6731)

Abstract

Topographic mapping offers a very flexible tool to inspect large quantities of high-dimensional data in an intuitive way. Often, electronic data are inherently non-Euclidean and modern data formats are connected to dedicated non-Euclidean dissimilarity measures for which classical topographic mapping cannot be used. We give an overview about extensions of topographic mapping to general dissimilarities by means of median or relational extensions. Further, we discuss efficient approximations to avoid the usually squared time complexity.

Keywords

Cost Function Topographic Mapping Dissimilarity Matrix Relational Cluster Median Cluster 
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 2011

Authors and Affiliations

  • Barbara Hammer
    • 1
  • Andrej Gisbrecht
    • 1
  • Alexander Hasenfuss
    • 2
  • Bassam Mokbel
    • 1
  • Frank-Michael Schleif
    • 1
  • Xibin Zhu
    • 1
  1. 1.CITEC centre of excellenceBielefeld UniversityGermany
  2. 2.Computing CentreTU ClausthalGermany

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