Skill Clustering
Skill Clustering is a computational method that groups granular skill representations based on semantic similarity or co-occurrence patterns, distinguishing it from broader skill taxonomies by its data-driven, unsupervised structuring of skills into coherent clusters.
30
Publications
8.7K
Citations
75
Authors
48
Institutions
Publications per year
2017–2026
9
Parents
30
1–5 of 30
75
4
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48
Carnegie Mellon University
Pittsburgh, United States
5 h-index
5 publications
142 citations
University of California, Berkeley
Berkeley, United States
3 h-index
3 publications
91 citations
Massachusetts Institute of Technology
Cambridge, United States
3 h-index
6 publications
14.6K citations

University of Glasgow
Glasgow, United Kingdom
2 h-index
2 publications
47 citations
IZA - Institute of Labor Economics
Bonn, Germany
2 h-index
4 publications
1.5K citations
Harvard University
Cambridge, United States
2 h-index
2 publications
7.2K citations

National Bureau of Economic Research
Cambridge, United States
2 h-index
4 publications
14.5K citations

Deutsche Bundesbank
Frankfurt am Main, Germany
1 h-index
1 publications
54 citations
1–8 of 48
4 publications · 114 citations
4
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Repository · 3 publications · 1.4K citations
3
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1 publications · 512 citations
1
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1 publications · 54 citations
1
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CAE · 1 publications · 32 citations
1
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1–5