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How to Visualize a Crisp or Fuzzy Topic Set over a Taxonomy

  • Boris Mirkin
  • Susana Nascimento
  • Trevor Fenner
  • Rui Felizardo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6744)

Abstract

A novel method for visualization of a fuzzy or crisp topic set is developed. The method maps the set’s topics to higher ranks of the taxonomy tree of the field. The method involves a penalty function summing penalties for the chosen “head subjects” together with penalties for emerging “gaps” and “offshoots”. The method finds a mapping minimizing the penalty function in recursive steps involving two different scenarios, that of ‘gaining a head subject’ and that of ‘not gaining a head subject’. We illustrate the method by applying it to illustrative and real-world data.

Keywords

Association Rule Penalty Function Spectral Cluster Taxonomy Tree Interior Node 
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

  • Boris Mirkin
    • 1
    • 2
  • Susana Nascimento
    • 3
  • Trevor Fenner
    • 2
  • Rui Felizardo
    • 3
  1. 1.Division of Applied Mathematics and InformaticsNational Research University - Higher School of EconomicsMoscowRussian Federation
  2. 2.Department of Computer ScienceBirkbeck University of LondonLondonUK
  3. 3.Department of Computer Science and Centre for Artificial Intelligence (CENTRIA), Faculdade de Ciências e TecnologiaUniversidade Nova de LisboaCaparicaPortugal

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