Extraction of Ideas from Microsystems Technology

Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 168)


In literature, idea mining is introduced as an approach that extracts interesting ideas from textual information. Idea mining research shows that the quality of the results strongly depends on the domain. This is because ideas from different domains consist of different properties. Related research has identified the idea properties for the medical domain and the social behavior domain. Based on these results, idea mining has been applied successfully in these two domains. In contrast to previous research, this work identifies the idea properties from a general technological domain to show that this domain differs from the two above mentioned domains and to show that idea mining also can applied successfully in a technological domain. Further, idea properties are identified by use of backward selection as main approach in stepwise regression, which is in contrast to previous research. Predictive variables are selected considering their statistical significance and a grid search is used to adapt the parameters of the idea mining algorithm. Microsystems technology is selected for a case study. It covers a wide range of different technologies because it is widely used in many technological areas. The case study shows that idea mining is successful in extracting new ideas from that domain.


Idea Mining Microsystems Technology Textmining Knowledge Discovery 


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  1. 1.
    Thorleuchter, D., Van den Poel, D.: High Granular Multi-Level-Security Model for Improved Usability. In: System Science, Engineering Design and Manufacturing Informatization (ICSEM 2011), pp. 191–194. IEEE Press, New York (2011)CrossRefGoogle Scholar
  2. 2.
    Wang, C., Lu, J., Zhang, G.: Mining key information of web pages: A method and its application. Expert Syst. Appl. 33(2), 425–433 (2007)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Thorleuchter, D., Van den Poel, D.: Semantic Technology Classification. In: Uncertainty Reasoning and Knowledge Engineering (URKE 2011), pp. 36–39. IEEE Press, New York (2011)CrossRefGoogle Scholar
  4. 4.
    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
  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.
    Park, Y., Lee, S.: How to design and utilize online customer center to support new product concept generation. Expert Syst. Appl. 38(8), 10638–10647 (2011)CrossRefGoogle Scholar
  7. 7.
    Thorleuchter, D., Van den Poel, D.: Companies Website Optimising concerning Consumer’s searching for new Products. In: Uncertainty Reasoning and Knowledge Engineering (URKE 2011), pp. 40–43. IEEE Press, New York (2011)CrossRefGoogle Scholar
  8. 8.
    Thorleuchter, D., Van den Poel, D., Prinzie, A.: Extracting Consumers Needs for New Products. In: Knowledge Discovery and Data Mining (WKDD 2010), pp. 440–443. IEEE Computer Society, Los Alamitos (2010)Google Scholar
  9. 9.
    Thorleuchter, D., Van den Poel, D., Prinzie, A.: Mining Ideas from Textual Information. Expert Syst. Appl. 37(10), 7182–7188 (2010)CrossRefGoogle Scholar
  10. 10.
    Thorleuchter, D., Herberz, S., Van den Poel, D.: Mining Social Behavior Ideas of Przewalski Horses. In: Wu, Y. (ed.) Advances in Computer, Communication, Control and Automation. LNEE, vol. 121, pp. 649–656. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  11. 11.
    Stumme, G., Hotho, A., Berendt, B.: Semantic Web Mining: State of the art and future directions. J. Web Semant. 4(2), 124–143 (2006)CrossRefGoogle Scholar
  12. 12.
    Van den Poel, D., Buckinx, W.: Predicting Online-Purchasing Behavior. Eur. J. Oper. Res. 166(2), 557–575 (2005)MATHCrossRefGoogle Scholar
  13. 13.
    Jiménez, Á.B., Lázaro, J.L., Dorronsoro, J.R.: Finding optimal model parameters by deterministic and annealed focused grid search. Neurocomputing 72(13-15), 2824–2832 (2009)CrossRefGoogle Scholar
  14. 14.
    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. 39(3), 2597–2605 (2012)CrossRefGoogle Scholar
  15. 15.
    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
  16. 16.
    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
  17. 17.
    Fluitman, J.: Microsystems technology: objectives. Sensors and Actuators A: Physical 56(1-2), 151–166 (1996)CrossRefGoogle Scholar
  18. 18.
    VDI/VDE Innovation + Technik GmbH: Mikrosystemtechnik, Innovation, Technik und Trends. Technologie & Management 1(2), 22–23 (2007)Google Scholar
  19. 19.
    VDI/VDE Innovation + Technik GmbH: Fortschritt mit System (June 09, 2010),

Copyright information

© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Fraunhofer INTEuskirchenGermany
  2. 2.Faculty of Economics and Business AdministrationGhent UniversityGentBelgium

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