SD-Map – A Fast Algorithm for Exhaustive Subgroup Discovery

  • Martin Atzmueller
  • Frank Puppe
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)


In this paper we present the novel SD-Map algorithm for exhaustive but efficient subgroup discovery. SD-Map guarantees to identify all interesting subgroup patterns contained in a data set, in contrast to heuristic or sampling-based methods. The SD-Map algorithm utilizes the well-known FP-growth method for mining association rules with adaptations for the subgroup discovery task. We show how SD-Map can handle missing values, and provide an experimental evaluation of the performance of the algorithm using synthetic data.


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  1. 1.
    Klösgen, W.: Explora: A Multipattern and Multistrategy Discovery Assistant. In: Fayyad, U.M., Piatetsky-Shapiro, G., Smyth, P., Uthurusamy, R. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI Press, Menlo Park (1996)Google Scholar
  2. 2.
    Klösgen, W.: 16.3: Subgroup Discovery. In: Handbook of Data Mining and Knowledge Discovery. Oxford University Press, Oxford (2002)Google Scholar
  3. 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. 4.
    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
  5. 5.
    Lavrac, N., Kavsek, B., Flach, P., Todorovski, L.: Subgroup Discovery with CN2-SD. Journal of Machine Learning Research 5, 153–188 (2004)MathSciNetGoogle Scholar
  6. 6.
    Friedman, J., Fisher, N.: Bump Hunting in High-Dimensional Data. Statistics and Computing 9(2) (1999)Google Scholar
  7. 7.
    Scheffer, T., Wrobel, S.: Finding the Most Interesting Patterns in a Database Quickly by Using Sequential Sampling. Journal of Machine Learning Research 3, 833–862 (2002)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns Without Candidate Generation. In: Chen, W., Naughton, J., Bernstein, P.A. (eds.) 2000 ACM SIGMOD Intl. Conference on Management of Data, pp. 1–12. ACM Press, New York (2000)CrossRefGoogle Scholar
  9. 9.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th Int. Conf. Very Large Data Bases (VLDB), pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  10. 10.
    Atzmueller, M., Puppe, F., Buscher, H.P.: Profiling Examiners using Intelligent Subgroup Mining. In: Proc. 10th Intl. Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP 2005), Aberdeen, Scotland, pp. 46–51 (2005)Google Scholar
  11. 11.
    Kavsek, B., Lavrac, N., Todorovski, L.: ROC Analysis of Example Weighting in Subgroup Discovery. In: Proc. 1st Intl. Workshop on ROC Analysis in AI, Valencia, Spain, pp. 55–60 (2004)Google Scholar
  12. 12.
    Kavsek, B., Lavrac, N., Jovanoski, V.: APRIORI-SD: Adapting Association Rule Learning to Subgroup Discovery. In: Berthold, M.R., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 230–241. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Liu, B., Hsu, W., Ma, Y.: Integrating Classification and Association Rule Mining. In: Proc. 4th Intl. Conference on Knowledge Discovery and Data Mining, New York, USA, pp. 80–86 (1998)Google Scholar
  14. 14.
    Zimmermann, A., Raedt, L.D.: CorClass: Correlated Association Rule Mining for Classification. In: Suzuki, E., Arikawa, S. (eds.) DS 2004. LNCS, vol. 3245, pp. 60–72. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  15. 15.
    Jovanoski, V., Lavrac, N.: Classification Rule Learning with APRIORI-C. In: Brazdil, P.B., Jorge, A.M. (eds.) EPIA 2001. LNCS, vol. 2258, pp. 44–51. Springer, Heidelberg (2001)Google Scholar
  16. 16.
    Zimmermann, A., Raedt, L.D.: Cluster-Grouping: From Subgroup Discovery to Clustering. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS, vol. 3201, pp. 575–577. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martin Atzmueller
    • 1
  • Frank Puppe
    • 1
  1. 1.Department of Computer ScienceUniversity of WürzburgWürzburgGermany

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