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Testing the static and dynamic performance of statistical methods for the detection of national industrial clusters

Datos Bibliográficos

ID12400793
AutoresFrancisco Benita (0000-0003-4867-5149, Singapore University of Technology and Design, autor de correspondencia), Serhad Sarica (0000-0003-0359-0360, Singapore University of Technology and Design), Garvit Bansal (Singapore University of Technology and Design)
Año2020
Volumen99
Número4
Páginas1137-1158
Fecha de publicación2020-02-07
Peer ReviewedSí
Open AccessSí
TipoARTICLE
RevistaPapers of the Regional Science Association (JOURNAL)
Identificadores de la revistaISSN: 1056-8190 • E-ISSN: 1435-5957
EditorialElsevier BV (PUBLISHER)
DOI10.1111/pirs.12517
OpenAlexW3004638177
IdiomaEN
Referencias citadas39

Cluster (spacecraft · Cluster analysis · Consistency (knowledge bases · Data mining · Fuzzy clustering · Fuzzy logic · Metric (unit · Operations management · Principal component analysis · Quality (philosophy · Complex Network Analysis Techniques · Computer Science · Engineering · Regional Economics and Spatial Analysis · Spatial and Panel Data Analysis · Artificial Intelligence

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