Hierarchical Clustering of Massive, High Dimensional Data Sets by Exploiting Ultrametric Embedding
From MaRDI portal
Publication:3617184
DOI10.1137/060676532zbMath1157.62041OpenAlexW1981556499MaRDI QIDQ3617184
Pedro Contreras, Fionn Murtagh, Geoff Downs
Publication date: 27 March 2009
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1137/060676532
Lua error in Module:PublicationMSCList at line 37: attempt to index local 'msc_result' (a nil value).
Related Items (9)
Lipschitz clustering in metric spaces ⋮ Direct reading algorithm for hierarchical clustering ⋮ Fast, linear time, \(m\)-adic hierarchical clustering for search and retrieval using the Baire metric, with linkages to generalized ultrametrics, hashing, formal concept analysis, and precision of data measurement ⋮ Symmetry in data mining and analysis: a unifying view based on hierarchy ⋮ Ultrametricity of Dissimilarity Spaces and Its Significance for Data Mining ⋮ Morphological Semigroups and Scale-Spaces on Ultrametric Spaces ⋮ Thinking Ultrametrically, Thinking p-Adically ⋮ The remarkable simplicity of very high dimensional data: application of model-based clustering ⋮ Fast, linear time hierarchical clustering using the Baire metric
This page was built for publication: Hierarchical Clustering of Massive, High Dimensional Data Sets by Exploiting Ultrametric Embedding
