Bisociative Knowledge Discovery

  • Michael R. Berthold
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7014)

Abstract

Data analysis generally focusses on finding patterns within a reasonably well connected domain of interest. In this article we focus on the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a novel way. We motivate this approach, show the difference to classical data analysis and conclude by briefly illustrating some types of domain-crossing connections along with illustrative examples.

Keywords

Connected Domain Information Repository Multi View Learn Computational Creativity Film Series 
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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References

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    Dubitzky, W., Kötter, T., Schmidt, O., Berthold, M.R.: Towards creative information exploration based on Koestler’s concept of bisociation. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery, 1st edn. LNCS. Springer, Heidelberg (in preparation)Google Scholar
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    Nagel, U., Thiel, K., Kötter, T., Piatek, D., Berthold, M.R.: Bisociative discovery of interesting relations between domains. In: Proceedings of IDA the 10th Conference on Intelligent Data Analysis, Portugal, Porto (in press)Google Scholar
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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Michael R. Berthold
    • 1
  1. 1.Nycomed Chair for Bioinformatics and Information Mining, Dept. of Computer and Information ScienceUniversity of KonstanzKonstanzGermany

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