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Analysis of Patient Groups and Immunization Results Based on Subspace Clustering

  • Michael Hund
  • Werner Sturm
  • Tobias Schreck
  • Torsten Ullrich
  • Daniel Keim
  • Ljiljana Majnaric
  • Andreas Holzinger
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9250)

Abstract

Biomedical experts are increasingly confronted with what is often called Big Data, an important subclass of high-dimensional data. High-dimensional data analysis can be helpful in finding relationships between records and dimensions. However, due to data complexity, experts are decreasingly capable of dealing with increasingly complex data. Mapping higher dimensional data to a smaller number of relevant dimensions is a big challenge due to the curse of dimensionality. Irrelevant, redundant, and conflicting dimensions affect the effectiveness and efficiency of analysis. Furthermore, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We show the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we analyze relationships between patients, sets of patient attributes, and outcomes of a vaccination treatment by means of a subspace clustering approach. We present an analysis workflow and discuss future directions for high-dimensional (medical) data analysis and visual exploration.

Keywords

Knowledge discovery and exploration Subspace clustering Subspace analysis Subspace classification Classification explanation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Michael Hund
    • 1
  • Werner Sturm
    • 2
  • Tobias Schreck
    • 2
  • Torsten Ullrich
    • 3
  • Daniel Keim
    • 1
  • Ljiljana Majnaric
    • 4
  • Andreas Holzinger
    • 5
    • 6
  1. 1.Data Analysis and Visualization GroupUniversity of KonstanzKonstanzGermany
  2. 2.Institute for Computer Graphics and Knowledge VisualizationGraz University of TechnologyGrazAustria
  3. 3.Fraunhofer Austria Reseach GmbHViennaAustria
  4. 4.Faculty of MedicineJJ Strossmayer University of OsijekOsijekCroatia
  5. 5.CBmed - Center for Biomarker Research in MedicineGrazAustria
  6. 6.Research Unit HCI-KDD, Institute for Medical Informatics, Statistics and DocumentationMedical University GrazGrazAustria

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