Research project grouping and ranking by using adaptive Mahalanobis clustering
| Autor: | Rudolf Scitovski, Damir Markulak, Željko Turkalj, Slavica Singer |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Statistics and Probability
Economics and Econometrics Computer science multi-criteria decision making 0211 other engineering and technologies project clustering adaptive Mahalanobis clustering evaluation 02 engineering and technology Management Science and Operations Research computer.software_genre Machine learning Expectation–maximization algorithm Special case Cluster analysis Mahalanobis distance 021103 operations research business.industry Applied Mathematics lcsh:T57-57.97 Review procedure Ranking lcsh:Applied mathematics. Quantitative methods Data mining Artificial intelligence Statistics Probability and Uncertainty business computer |
| Zdroj: | Croatian Operational Research Review Volume 7 Issue 1 Croatian Operational Research Review, Vol 7, Iss 1, Pp 81-96 (2016) |
| ISSN: | 1848-0225 1848-9931 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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