Hierarchical Clustering better than Average-Linkage

DOI10.1137/1.9781611975482.139zbMath1432.68568arXiv1808.02227OpenAlexW2952682756MaRDI QIDQ5236326

Rad Niazadeh, Vaggos Chatziafratis, Moses Charikar

Publication date: 15 October 2019

Published in: Proceedings of the Thirtieth Annual ACM-SIAM Symposium on Discrete Algorithms (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.02227

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Approximation algorithms (68W25)

Related Items (4)

On Dasgupta's hierarchical clustering objective and its relation to other graph parameters ⋮ Expanding the class of global objective functions for dissimilarity-based hierarchical clustering ⋮ Maximizing the ratio of cluster split to cluster diameter without and with cardinality constraints ⋮ Ultrametric fitting by gradient descent ^*

This page was built for publication: Hierarchical Clustering better than Average-Linkage