Identifying industrial clusters with a novel big-data methodology: are SIC codes (not) fit for purpose in the internet age?

DOI10.1016/j.cor.2017.06.010zbMath1391.90401OpenAlexW2631762373MaRDI QIDQ1651685

Yang Yang, Savvas Papagiannidis, Eric W. K. See-To, Dimitris G. Assimakopoulos

Publication date: 12 July 2018

Published in: Computers \& Operations Research (Search for Journal in Brave)

Full work available at URL: https://eprint.ncl.ac.uk/fulltext.aspx?url=238842/4064DC55-3A3E-46F4-8D9C-3BF0F6F69653.pdf&pub_id=238842

zbMATH Keywords

clustersoperationsbig data analyticsregional policyindustry classificationNorth East of EnglandSIC codesstrategic co-operation

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics in engineering and industry; control charts (62P30) Semidefinite programming (90C22) Mixed integer programming (90C11) Combinatorial optimization (90C27) Case-oriented studies in operations research (90B90) Internet topics (68M11)

Related Items (1)

Effects of informal contracts on innovative cooperation among enterprises in industrial clusters: an evolutionary game analysis

Cites Work

• A mixed integer linear model for clustering with variable selection
• Computational results of a semidefinite branch-and-bound algorithm for \(k\)-cluster
• 10.1162/jmlr.2003.3.4-5.993

This page was built for publication: Identifying industrial clusters with a novel big-data methodology: are SIC codes (not) fit for purpose in the internet age?