4th International Conference on Computer Science and Engineering (UBMK), Samsun, Türkiye, 11 - 15 Eylül 2019, ss.580-584
Research areas at large universities with a student body of 50K+ often include a wide range of disciplines including Social Sciences, Natural and Applied Sciences, Health Sciences, Fine Arts and Athletics. In order to increase collaboration and determine areas of strength requires correct clustering of scientist and research areas within a research organization. Grouping of researchers based on department/program may not be the best approach as science is increasingly becoming more interdisciplinary [1]. Therefore, the purpose of this study is to introduce a data mining approach to cluster scientist by research areas using similarity measures at a large institute.