Abstract

Given a binary relation R, we look for partitions of its row set and its column set, respectively, that behave well with respect to selected algebraic properties, i.e., correspond to congruences related to R. Permutations are derived from these congruences that allow to rearrange R visualizing the decomposition.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Džeroski, S., Lavrač, N. (eds.): Relational Data Mining. Springer, Heidelberg (2001)MATHGoogle Scholar
  2. 2.
    Hudak, P., Jones, S.L.P., Wadler, P., et al.: Report on the programming language Haskell, a non-strict purely functional language, version 1.2. ACM SIGPLAN Notices 27(5) (1992), see also http://haskell.org/
  3. 3.
    Kitainik, L.: Fuzzy Decision Procedures With Binary Relations — Towards a unified theory. Theory and Decision Library, Series D: System Theory, Knowledge Engineering and Problem Solving, vol. 13. Kluwer Academic Publishers, Dordrecht (1993)MATHGoogle Scholar
  4. 4.
    Riguet, J.: Quelques propriétés des relations difonctionelles. C. R. Acad. Sci. Paris 230, 1999–2000 (1950)MATHMathSciNetGoogle Scholar
  5. 5.
    Schmidt, G., Ströhlein, T.: Relationen und Graphen. Mathematik für Informatiker. Springer, Heidelberg (1989) ISBN 3-540-50304-8, ISBN 0-387-50304-8. 232Google Scholar
  6. 6.
    Schmidt, G., Ströhlein, T.: Relations and Graphs – Discrete Mathematics for Computer Scientists. EATCS Monographs on Theoretical Computer Science. Springer, Heidelberg (1993) ISBN 3-540-56254-0, ISBN 0-387-56254-0. 232MATHGoogle Scholar
  7. 7.
    Swift, J.: Gulliver’s Travels. 1726Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Gunther Schmidt
    • 1
  1. 1.Institute for Software Technology, Department of Computing ScienceFederal Armed Forces University Munich 

Personalised recommendations