A novel scale-invariant, dynamic method for hierarchical clustering of data affected by measurement uncertainty

DOI10.1016/j.cam.2018.05.062zbMath1460.62100OpenAlexW2805835535MaRDI QIDQ724539

Alfonso Niro, Federica Vignati, Damiano Fustinoni

Publication date: 26 July 2018

Published in: Journal of Computational and Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cam.2018.05.062

zbMATH Keywords

uncertaintyhierarchical clusteringnon-uniquenessproximity measurecomputational cost

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Artificial intelligence (68T99)

Related Items (1)

A data envelopment analysis-based clustering approach under dynamic situations

Cites Work

• Solving non-uniqueness in agglomerative hierarchical clustering using multidendrograms
• Scale-invariant clustering with minimum volume ellipsoids
• Hub Location and the p-Hub Median Problem
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