Research project grouping and ranking by using adaptive Mahalanobis clustering

  • Željko Turkalj Faculty of Economics, University of Osijek
  • Damir Markulak Faculty of Civil Engineering, University of Osijek
  • Slavica Singer Faculty of Economics, University of Osijek
  • Rudolf Scitovski University of Josip Juraj Strossmayer in Osijek, Department of Mathematics, Osijek

Abstract

The paper discusses the problem of grouping and ranking of research projects submitted for a call. The projects are grouped into clusters based on the assessment obtained in the review procedure and by using the adaptive Mahalanobis clustering method as a special case of the Expectation Maximization algorithm. The cluster of projects assessed as best is specially analyzed and ranked. The paper outlines several possibilities for the use of data obtained in the review procedure, and the proposed method is illustrated with the example of internal research projects at the University of Osijek.

Author Biography

Rudolf Scitovski, University of Josip Juraj Strossmayer in Osijek, Department of Mathematics, Osijek
Department of Mathematics
Published
2016-04-01
Section
CRORR Journal Regular Issue