Skip to main content
SpringerLink
Log in

On subgroup discovery in numerical domains

  • Published:
Data Mining and Knowledge Discovery Aims and scope Submit manuscript

Abstract

Subgroup discovery is a Knowledge Discovery task that aims at finding subgroups of a population with high generality and distributional unusualness. While several subgroup discovery algorithms have been presented in the past, they focus on databases with nominal attributes or make use of discretization to get rid of the numerical attributes. In this paper, we illustrate why the replacement of numerical attributes by nominal attributes can result in suboptimal results. Thereafter, we present a new subgroup discovery algorithm that prunes large parts of the search space by exploiting bounds between related numerical subgroup descriptions. The same algorithm can also be applied to ordinal attributes. In an experimental section, we show that the use of our new pruning scheme results in a huge performance gain when more that just a few split-points are considered for the numerical attributes.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Asuncion A, Newman DJ (2007) UCI machine learning repository. University of California, Irvine, School of Information and Computer Sciences

  • Atzmueller M, Puppe F (2005) Semi-automatic visual subgroup mining using vikamine. J Univers Comp Sci 11(11): 1752–1765

    Google Scholar 

  • Atzmüller M, Puppe F (2006) SD-map—a fast algorithm for exhaustive subgroup discovery. In: Fürnkranz J, Scheffer T, Spiliopoulou M (eds) PKDD, volume 4213 of lecture notes in computer science. Springer, New York, pp 6–17

    Google Scholar 

  • Demsar J, Zupan B, Leban G (2004) Orange: from experimental machine learning to interactive data mining. Technical report, faculty of computer and information science. University of Ljubljana, Slovenia

  • Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. Morgan Kaufmann, Los Altos, pp 194–202

  • Fayyad UM, Irani KB (1993) Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI, pp 1022–1029

  • Grosskreutz H, Rüping S, Wrobel S (2008) Tight optimistic estimates for fast subgroup discovery. In: Daelemans W, Goethals B, Morik K (eds) ECML/PKDD (1), volume 5211 of lecture notes in computer science. Springer, New York, pp 440–456

    Google Scholar 

  • Hapfelmeier A, Schmidt J, Mueller M, Perneczky R, Drzezga A, Kurz A, Kramer S (2008) Interpreting pet scans by structured patient data: a data mining case study in dementia research. In: Proceedings of the eighth IEEE international conference on data mining (ICDM-2008)

  • Klösgen W (1996) Explora: a multipattern and multistrategy discovery assistant. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI/MIT Press, pp 249–271. ISBN 0-262-56097-6

  • Klösgen W, May M (2002) Spatial subgroup mining integrated in an object-relational spatial database. In: PKDD ’02. Springer-Verlag, London, pp 275–286

  • Kralj P, Lavrač N, Zupan B, Gamberger D (2005) Experimental comparison of three subgroup discovery algorithms: Analysing brain ischemia data. In: Proceedings of the 8th International multiconference information society IS 2005, pp 220–223

  • Lavrac N, Gamberger D (2004) Relevancy in constraint-based subgroup discovery. In: Boulicaut J-F, Raedt LD, Mannila H (eds) Constraint-based mining and inductive databases, volume 3848 of lecture notes in computer science. Springer, New York, pp 243–266

    Google Scholar 

  • Lavrac N, Flach PA, Kavsek B, Todorovski L (2002) Adapting classification rule induction to subgroup discovery. In: Proceedings of the 2002 IEEE international conference on data mining (ICDM 2002), 9–12 December 2002, Maebashi City, Japan. IEEE Computer Society, pp 266–273

  • Lavrac N, Cestnik B, Gamberger D, Flach PA (2004) Decision support through subgroup discovery: three case studies and the lessons learned. Mach Learn 57(1–2): 115–143

    Article  MATH  Google Scholar 

  • Srikant R, Agrawal R (1996) Mining quantitative association rules in large relational tables. ACM SIGMOD Record 25(2): 1–12

    Article  Google Scholar 

  • Webb GI (2001) Discovering associations with numeric variables. In: KDD ’01: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, New York, pp 383–388

  • Wrobel S (1997) An algorithm for multi-relational discovery of subgroups. In: Komorowski J, Zytkow J (eds) Proceedings of the first European symposion on principles of data mining and knowledge discovery (PKDD-97). Springer, New York, pp 78–87

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Fraunhofer IAIS, Schloss Birlinghoven, Sankt Augustin, Germany

    Henrik Grosskreutz & Stefan Rüping

Authors
  1. Henrik Grosskreutz
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Rüping
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Henrik Grosskreutz.

Additional information

Responsible editor: Aleksander Kołcz, Wray Buntine, Marko Grobelnik, Dunja Mladenic, and John Shawe-Taylor.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Grosskreutz, H., Rüping, S. On subgroup discovery in numerical domains. Data Min Knowl Disc 19, 210–226 (2009). https://doi.org/10.1007/s10618-009-0136-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10618-009-0136-3

Keywords

Search

Navigation