@incollection{Berthold2012Towar-19466,
  year={2012},
  doi={10.1007/978-3-642-31830-6_1},
  title={Towards Bisociative Knowledge Discovery},
  number={7250},
  isbn={978-3-642-31829-0},
  publisher={Springer Berlin Heidelberg},
  address={Berlin, Heidelberg},
  series={Lecture Notes in Computer Science},
  booktitle={Bisociative Knowledge Discovery},
  pages={1--10},
  editor={Berthold, Michael R.},
  author={Berthold, Michael R.},
  note={Open Access}
}

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    <dc:creator>Berthold, Michael R.</dc:creator>
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    <dcterms:bibliographicCitation>Bisociative Knowledge Discovery : An Introduction to Concept, Algorithms, Tools, and Applications / Michael R. Berthold (ed.). - Heidelberg [u.a.] : Springer, 2012. - S. 1-10. - (Lecture Notes in Computer Science ; 7250 : Lecture notes in artificial intelligence). - 978-3-642-31829-0</dcterms:bibliographicCitation>
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    <dcterms:abstract xml:lang="eng">Knowledge discovery generally focuses on finding patterns within a reasonably well connected domain of interest. In this article we outline a framework for the discovery of new connections between domains (so called bisociations), supporting the creative discovery process in a more powerful way. We motivate this approach, show the difference to classical data analysis and conclude by describing a number of different types of domain-crossing connections.</dcterms:abstract>
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