Supporting factors to improve the explanatory potential of contrast set mining: Analyzing brain ischaemia data

N. Lavrac, Petra Kralj*, D. Gamberger, A. Krstacic

*Corresponding author for this work

Research output: Contribution to journalConference articlepeer-review

Abstract (may include machine translation)

The goal of exploratory pattern mining is to find patterns that exhibit yet unknown relationships in data and to provide insightful representations of detected relationships. This paper explores contrast set mining and an approach to improving its explanatory potential by using the so called supporting factors that provide additional descriptions of the detected patterns. The proposed methodology is described in a medical data analysis problem of distinguishing between similar diseases in the analysis of patients suffering from brain ischaemia.

Original languageEnglish
Pages (from-to)157-161
Number of pages5
JournalIFMBE Proceedings
Volume16
Issue number1
StatePublished - 2007
Externally publishedYes
Event11th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2007 - Ljubljana, Slovenia
Duration: 26 Jun 200730 Jun 2007

Keywords

  • Brain ischaemia
  • Contrast set mining
  • Exploratory data analysis
  • Subgroup discovery
  • Supporting factors

Fingerprint

Dive into the research topics of 'Supporting factors to improve the explanatory potential of contrast set mining: Analyzing brain ischaemia data'. Together they form a unique fingerprint.

Cite this