Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering

Single-linkage clustering is a popular form of hierarchical agglomerative clustering (HAC) where the distance between two clusters is defined as the minimum distance between any pair of points across the two clusters. In single-linkage HAC, the output is typically the single-linkage dendrogram (SLD), which is the binary tree representing the hierarchy of clusters formed by iteratively contracting the two closest clusters. In the dynamic setting, prior work has only studied maintaining a minimum spanning forest over the data since single-linkage HAC reduces to computing the SLD on the minimum spanning forest of the data.

Authors

Institutions

Publication Details

Published
2025-07-16
DOI
https://doi.org/10.1145/3694906.3743315
Citations
1
Primary Topic
Graph Theory and Algorithms
Type
article
Field-Weighted Citation Impact
1.28

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering

Kishen N. Gowda, Laxman Dhulipala, Quinten De Man
1 citations
Graph Theory and Algorithms
1.28
article

Fully-Dynamic Parallel Algorithms for Single-Linkage Clustering

Kishen N. Gowda, Laxman Dhulipala, Quinten De Man
article en
1 citations

Abstract

Single-linkage clustering is a popular form of hierarchical agglomerative clustering (HAC) where the distance between two clusters is defined as the minimum distance between any pair of points across the two clusters. In single-linkage HAC, the output is typically the single-linkage dendrogram (SLD), which is the binary tree representing the hierarchy of clusters formed by iteratively contracting the two closest clusters. In the dynamic setting, prior work has only studied maintaining a minimum spanning forest over the data since single-linkage HAC reduces to computing the SLD on the minimum spanning forest of the data.

University of Maryland, College Park (US)
National Science Foundation
Openalex Percentile: Top 18%
Graph Theory and Algorithms
1.28
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.