Subgroup Discovery in Data Sets with Multi–dimensional Responses: A Method and a Case Study in Traumatology

  • Lan Umek
  • Blaž Zupan
  • Marko Toplak
  • Annie Morin
  • Jean-Hugues Chauchat
  • Gregor Makovec
  • Dragica Smrke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5651)

Abstract

Biomedical experimental data sets may often include many features both at input (description of cases, treatments, or experimental parameters) and output (outcome description). State-of-the-art data mining techniques can deal with such data, but would consider only one output feature at the time, disregarding any dependencies among them. In the paper, we propose the technique that can treat many output features simultaneously, aiming at finding subgroups of cases that are similar both in input and output space. The method is based on k-medoids clustering and analysis of contingency tables, and reports on case subgroups with significant dependency in input and output space. We have used this technique in explorative analysis of clinical data on femoral neck fractures. The subgroups discovered in our study were considered meaningful by the participating domain expert, and sparked a number of ideas for hypothesis to be further experimentally tested.

Keywords

subgroup discovery multi–label prediction k-medoids clustering χ2 statistics femoral neck fracture 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Lan Umek
    • 1
  • Blaž Zupan
    • 1
    • 2
  • Marko Toplak
    • 1
  • Annie Morin
    • 3
  • Jean-Hugues Chauchat
    • 4
  • Gregor Makovec
    • 5
  • Dragica Smrke
    • 5
  1. 1.Faculty of Computer and Information SciencesUniversity of LjubljanaSlovenia
  2. 2.Dept. of Human and Mol. GeneticsBaylor College of MedicineHoustonUSA
  3. 3.IRISA, Universite de Rennes 1Rennes cedexFrance
  4. 4.Universite de Lyon, ERIC-Lyon 2Bron CedexFrance
  5. 5.Dept. of TraumatologyUniversity Clinical CentreLjubljanaSlovenia

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