Bisociative Knowledge Discovery pp 230-245

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7250) | Cite as

(Missing) Concept Discovery in Heterogeneous Information Networks

  • Tobias Kötter
  • Michael R. Berthold

Abstract

This article proposes a new approach to extract existing (or detect missing) concepts from a loosely integrated collection of information units by means of concept graph detection. Thereby a concept graph defines a concept by a quasi bipartite sub-graph of a bigger network with the members of the concept as the first vertex partition and their shared aspects as the second vertex partition. Once the concepts have been extracted they can be used to create higher level representations of the data. Concept graphs further allow the discovery of missing concepts, which could lead to new insights by connecting seemingly unrelated information units.

References

  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (1994)Google Scholar
  2. 2.
    Beach, L.R.: Cue probabilism and inference behavior. Psychological Monographs: General and Applied 78, 1–20 (1964)CrossRefGoogle Scholar
  3. 3.
    Berthold, M.R., Dill, F., Kötter, T., Thiel, K.: Supporting Creativity: Towards Associative Discovery of New Insights. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 14–25. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Jaccard, P.: Étude comparative de la distribution florale dans une portion des alpes et des jura. Bulletin de la Société Vaudoise des Sciences Naturells 37, 547–579 (1901)Google Scholar
  5. 5.
    Koestler, A.: The Act of Creation. Macmillan (1964)Google Scholar
  6. 6.
    Kötter, T., Berthold, M.R.: (Missing) concept discovery in heterogeneous information networks. In: Proceedings of the 2nd International Conference on Computational Creativity, pp. 135–140 (2011)Google Scholar
  7. 7.
    Kötter, T., Berthold, M.R.: From Information Networks to Bisociative Information Networks. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), vol. 7250, pp. 33–50. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  8. 8.
    Kötter, T., Thiel, K., Berthold, M.R.: Domain bridging associations support creativity. In: Proceedings of the International Conference on Computational Creativity, pp. 200–204 (2010)Google Scholar
  9. 9.
    Rosch, E., Mervis, C.B., Gray, W.D., Johnson, D.M., Boyes-Braem, P.: Basic objects in natural categories. Cognitive Psychology 8, 382–439 (1976)CrossRefGoogle Scholar
  10. 10.
    Segond, M., Borgelt, C.: Item Set Mining Based on Cover Similarity. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 493–505. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Wille, R.: Restructuring lattice theory: An approach based on hierarchies of concepts. In: Ordered Sets, pp. 314–339 (1982)Google Scholar
  12. 12.
    Wittgenstein, L.: Philosophical Investigations. Blackwell, Oxford (1953)MATHGoogle Scholar
  13. 13.
    Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (1997)Google Scholar

Copyright information

© The Author(s) 2012 2012

Authors and Affiliations

  • Tobias Kötter
    • 1
  • Michael R. Berthold
    • 1
  1. 1.Nycomed-Chair for Bioinformatics and Information MiningUniversity of KonstanzKonstanzGermany

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