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Journal of Intelligent Information Systems

, Volume 44, Issue 2, pp 271–288 | Cite as

Efficient redundancy reduced subgroup discovery via quadratic programming

  • Rui Li
  • Robert Perneczky
  • Alexander Drzezga
  • Stefan KramerEmail author
Article

Abstract

Subgroup discovery is a task at the intersection of predictive and descriptive induction, aiming at identifying subgroups that have the most unusual statistical (distributional) characteristics with respect to a property of interest. Although a great deal of work has been devoted to the topic, one remaining problem concerns the redundancy of subgroup descriptions, which often effectively convey very similar information. In this paper, we propose a quadratic programming based approach to reduce the amount of redundancy in the subgroup rules. Experimental results on 12 datasets show that the resulting subgroups are in fact less redundant compared to standard methods. In addition, our experiments show that the computational costs are significantly lower than the costs of other methods compared in the paper.

Keywords

Subgroup discovery Mutual information Quadratic programming Rule learning Redundancy 

Notes

Acknowledgement

The first author acknowledges the support of the TUM Graduate School of Information Science in Health (GSISH), Technische Universität München.

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Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Rui Li
    • 1
  • Robert Perneczky
    • 2
    • 3
    • 4
  • Alexander Drzezga
    • 5
  • Stefan Kramer
    • 6
    Email author
  1. 1.Institut für Informatik/I12Technische Universität MünchenGarching b. MünchenGermany
  2. 2.Neuroepidemiology and Ageing Research Unit, School of Public Health, Faculty of MedicineThe Imperial College of Science, Technology and MedicineLondonUK
  3. 3.Klinik und Poliklinik für Psychiatrie und PsychotherapieTechnische Universität MünchenMünchenGermany
  4. 4.West London Cognitive Disorders Treatment and Research Unit, West London Mental Health TrustLondonUK
  5. 5.Klinik und Poliklinik für NuklearmedizinUniversität zu KölnKölnGermany
  6. 6.Institut für InformatikJohannes Gutenberg - Universität MainzMainzGermany

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