Mining Social Behavior Ideas of Przewalski Horses

  • Dirk Thorleuchter
  • Sarah Herberz
  • Dirk Van den Poel
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 121)


Literature introduces idea mining as an approach for extracting interesting ideas from textual information. Related research focuses on extracting technological ideas as starting point for future technological research and development activities. Thus, it is limited to the technological domain. The algorithms standing behind idea mining also are optimized for the technological domain.

In contrast to previous research, this work transfers idea mining to the social behavior domain by selecting and adapting parameters of the idea mining algorithm. Forward selection as main approach in stepwise regression is used to choose the predictive variables based on their statistical significance. Grid search is used to optimize the parameter values. A case study shows that these optimized idea mining parameters are successful in extracting social behavior ideas of animals in this case of Przewalski horses. Based on these findings, differences between technological ideas and social behavior ideas can be shown.


Idea Mining Social Behavior Przewalski Horses Textmining Knowledge Discovery 


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  1. 1.
    Thorleuchter, D., Van den Poel, D., Prinzie, A.: Mining Ideas from Textual Information. Expert Syst. Appl. 37(10), 7182–7188 (2010)CrossRefGoogle Scholar
  2. 2.
    Thorleuchter, D., Van den Poel, D., Prinzie, A.: A compared R&D-based and patent-based cross impact analysis for identifying relationships between technologies. Technol. Forecast. Soc. Change 77(7), 1037–1050 (2010)CrossRefGoogle Scholar
  3. 3.
    Thorleuchter, D., Van den Poel, D., Prinzie, A.: Extracting Consumers Needs for New Products. In: Proceedings WKDD 2010, pp. 440–443. IEEE Computer Society, Los Alamitos (2010)Google Scholar
  4. 4.
    Thorleuchter, D., Van den Poel, D.: Companies Website Optimising concerning Consumer’s searching for new Products. In: 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering. IEEE Press, New York (2011)Google Scholar
  5. 5.
    Thorleuchter, D., Van den Poel, D., Prinzie, A.: Mining Innovative Ideas to Support new Product Research and Development. In: Locarek-Junge, H., Weihs, C. (eds.) Classification as a Tool for Research, pp. 587–594. Springer, Berlin (2010)CrossRefGoogle Scholar
  6. 6.
    Thorleuchter, D., Van den Poel, D.: Semantic Technology Classification. In: 2011 International Conference on Uncertainty Reasoning and Knowledge Engineering. IEEE Press, New York (2011)Google Scholar
  7. 7.
    Van den Poel, D., Buckinx, W.: Predicting Online-Purchasing Behavior. Eur. J. Oper. Res. 166(2), 557–575 (2005)zbMATHCrossRefGoogle Scholar
  8. 8.
    Thorleuchter, D., Van den Poel, D., Prinzie, A.: Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing. Expert Syst. Appl. (in press, 2012) Google Scholar
  9. 9.
    Dierendonck, M.C., van Bandi, M., Batdorj, D., Dügerlham, S., Munkhtsog, B.: Behavioural observations of reintroduced takhi or Przewalski horses (Equus ferus przewalskii) in Mongolia. Appl. Anim. Behav. Sci. 50(2), 95–114 (1996)CrossRefGoogle Scholar
  10. 10.
    Thorleuchter, D.: Finding New Technological Ideas and Inventions with Text Mining and Technique Philosophy. In: Preisach, C., Burkhardt, H., Schmidt-Thieme, L., Decker, R. (eds.) Data Analysis, Machine Learning and Applications, pp. 413–420. Springer, Berlin (2008)CrossRefGoogle Scholar
  11. 11.
    Kim, Y.S.: Toward a successful CRM: variable selection, sampling and ensemble. Decis. Support Syst. 41(2), 542–553 (2006)CrossRefGoogle Scholar
  12. 12.
    Coussement, C., Van den Poel, D.: Churn prediction in subscription services: An application of support vector machines while comparing two parameter-selection techniques. Expert Syst. Appl. 34(1), 313–327 (2008)CrossRefGoogle Scholar
  13. 13.
    Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A Practical Guide to Support Vector Classification. In: Technical report, Department of Computer Science and Information Engineering, National Taiwan University (2004)Google Scholar
  14. 14.
    Thorleuchter, D., Van den Poel, D.: High Granular Multi-Level-Security Model for Improved Usability. In: Proceedings ICSEM 2011. IEEE Press, New York (2011); ISBN: 978-1-4577-0245-7 Google Scholar
  15. 15.
    Kolter, L., Zimmermann, W.: Die Haltung von Junggesellengruppen für das EEP Przewalskipferd - Hengste in Gehegen und Reservaten. Zeitschrift des Kölner Zoo 44(3), 135–151 (2001)Google Scholar
  16. 16.
    Herberz, S.: Dominanzverhalten bei Przewalskipferden in seminatürlicher Haltung im Kölner Zoo unter besonderer Berücksichtigung der Körpersprache. Fraunhofer INT edition, Euskirchen (2011)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dirk Thorleuchter
    • 1
  • Sarah Herberz
    • 1
  • Dirk Van den Poel
    • 2
  1. 1.Fraunhofer INTEuskirchenGermany
  2. 2.Faculty of Economics and Business AdministrationGhent UniversityGentBelgium

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