Expanding the class of global objective functions for dissimilarity-based hierarchical clustering

DOI10.1007/s00357-023-09447-xarXiv2207.14375MaRDI QIDQ6187790

Sebastien Roch

Publication date: 1 February 2024

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2207.14375

zbMATH Keywords

hierarchical clusteringgreedy algorithmsminimum evolution

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Problems related to evolution (92D15) General topics of discrete mathematics in relation to computer science (68R01)

Cites Work

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• Minimum evolution using ordinary least-squares is less robust than neighbor-joining
• Approximating the balanced minimum evolution problem
• On the uniqueness of the selection criterion in neighbor-joining
• Why neighbor-joining works
• The performance of neighbor-joining methods of phylogenetic reconstruction
• A signal-to-noise analysis of phylogeny estimation by neighbor-joining: Insufficiency of polynomial length sequences
• Phylogeny
• Combinatorics of least-squares trees
• Toward Extracting All Phylogenetic Information from Matrices of Evolutionary Distances
• Approximate Hierarchical Clustering via Sparsest Cut and Spreading Metrics
• Computational Phylogenetics
• Hierarchical Clustering better than Average-Linkage
• Likelihood Based Hierarchical Clustering
• A cost function for similarity-based hierarchical clustering

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