Expected-time complexity results for hierarchic clustering algorithms which use cluster centres

DOI10.1016/0020-0190(83)90095-9zbMath0508.68024OpenAlexW1969242249MaRDI QIDQ1838036

Fionn Murtagh

Publication date: 1983

Published in: Information Processing Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0020-0190(83)90095-9

zbMATH Keywords

probabilistic algorithmsnearest neighboursdynamic searching

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Analysis of algorithms and problem complexity (68Q25) Searching and sorting (68P10) Data structures (68P05) Information storage and retrieval of data (68P20)

Related Items (4)

Optical clustering ⋮ Clustering methods for geometric objects and applications to design problems ⋮ Average complexity of divide-and-conquer algorithms ⋮ 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

Cites Work

• A probabilistic minimum spanning tree algorithm
• Optimal Expected-Time Algorithms for Closest Point Problems
• Reflexive Nearest Neighbours
• Fast Algorithms for Constructing Minimal Spanning Trees in Coordinate Spaces
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