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A Methodological View on Knowledge-Intensive Subgroup Discovery

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 4248))

Abstract

Background knowledge is a natural resource for knowledge-intensive methods: Its exploitation can often improve the quality of their results significantly. In this paper we present a methodological view on knowledge-intensive subgroup discovery: We introduce different classes and specific types of useful background knowledge, discuss their benefit and costs, and describe their application in the subgroup discovery setting.

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References

  1. Richardson, M., Domingos, P.: Learning with Knowledge from Multiple Experts. In: Proc. 20th Intl. Conference on Machine Learning (ICML 2003), pp. 624–631. AAAI Press, Menlo Park (2003)

    Google Scholar 

  2. Klösgen, W.: Explora: A Multipattern and Multistrategy Discovery Assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI Press, Menlo Park (1996)

    Google Scholar 

  3. Wrobel, S.: An Algorithm for Multi-Relational Discovery of Subgroups. In: Komorowski, J., Żytkow, J.M. (eds.) PKDD 1997. LNCS, vol. 1263, pp. 78–87. Springer, Heidelberg (1997)

    Google Scholar 

  4. Baumeister, J., Atzmueller, M., Puppe, F.: Inductive Learning for Case-Based Diagnosis with Multiple Faults. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 28–42. Springer, Heidelberg (2002)

    Chapter  Google Scholar 

  5. Atzmueller, M., Puppe, F., Buscher, H.-P.: Exploiting Background Knowledge for Knowledge-Intensive Subgroup Discovery. In: Proc. 19th Intl. Joint Conference on Artificial Intelligence (IJCAI 2005), Edinburgh, Scotland, pp. 647–652 (2005)

    Google Scholar 

  6. Zelezny, F., Lavrac, N., Dzeroski, S.: Using Constraints in Relational Subgroup Discovery. In: Intl. Conference on Methodology and Statistics, pp. 78–81. University of Ljubljana (2003)

    Google Scholar 

  7. Boulicaut, J.-F., Jeudy, B.: Constraint-based data mining. In: The Data Mining and Knowledge Discovery Handbook. Springer, Heidelberg (2005)

    Google Scholar 

  8. Liu, B., Hsu, W.: Post-Analysis of Learned Rules. In: Proc. 13th National Conference on Artificial Intelligence (AAAI 1996), pp. 828–834. AAAI Press, Menlo Park (1996)

    Google Scholar 

  9. Atzmüller, M., Baumeister, J., Hemsing, A., Richter, E.-J., Puppe, F.: Subgroup Mining for Interactive Knowledge Refinement. In: Miksch, S., Hunter, J., Keravnou, E.T. (eds.) AIME 2005. LNCS (LNAI), vol. 3581, pp. 453–462. Springer, Heidelberg (2005)

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© 2006 Springer-Verlag Berlin Heidelberg

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Atzmueller, M., Puppe, F. (2006). A Methodological View on Knowledge-Intensive Subgroup Discovery. In: Staab, S., Svátek, V. (eds) Managing Knowledge in a World of Networks. EKAW 2006. Lecture Notes in Computer Science(), vol 4248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11891451_28

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  • DOI: https://doi.org/10.1007/11891451_28

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-46363-4

  • Online ISBN: 978-3-540-46365-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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