Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs

Steinar Laenen, Bogdan Adrian Manghiuc, He Sun
Proceedings of the 40th International Conference on Machine Learning, PMLR 202:18207-18249, 2023.

Abstract

This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta’s cost function. For any input graph $G$ with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of $G$, and return an $O(1)$-approximate HC tree with respect to Dasgupta’s cost function. We compare the performance of our algorithm against the previous state-of-the-art on synthetic and real-world datasets and show that our designed algorithm produces comparable or better HC trees with much lower running time.

Cite this Paper

BibTeX
@InProceedings{pmlr-v202-laenen23a, title = {Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs}, author = {Laenen, Steinar and Manghiuc, Bogdan Adrian and Sun, He}, booktitle = {Proceedings of the 40th International Conference on Machine Learning}, pages = {18207--18249}, year = {2023}, editor = {Krause, Andreas and Brunskill, Emma and Cho, Kyunghyun and Engelhardt, Barbara and Sabato, Sivan and Scarlett, Jonathan}, volume = {202}, series = {Proceedings of Machine Learning Research}, month = {23--29 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v202/laenen23a/laenen23a.pdf}, url = {https://proceedings.mlr.press/v202/laenen23a.html}, abstract = {This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta’s cost function. For any input graph $G$ with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of $G$, and return an $O(1)$-approximate HC tree with respect to Dasgupta’s cost function. We compare the performance of our algorithm against the previous state-of-the-art on synthetic and real-world datasets and show that our designed algorithm produces comparable or better HC trees with much lower running time.} }
Endnote
%0 Conference Paper %T Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs %A Steinar Laenen %A Bogdan Adrian Manghiuc %A He Sun %B Proceedings of the 40th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2023 %E Andreas Krause %E Emma Brunskill %E Kyunghyun Cho %E Barbara Engelhardt %E Sivan Sabato %E Jonathan Scarlett %F pmlr-v202-laenen23a %I PMLR %P 18207--18249 %U https://proceedings.mlr.press/v202/laenen23a.html %V 202 %X This paper presents two efficient hierarchical clustering (HC) algorithms with respect to Dasgupta’s cost function. For any input graph $G$ with a clear cluster-structure, our designed algorithms run in nearly-linear time in the input size of $G$, and return an $O(1)$-approximate HC tree with respect to Dasgupta’s cost function. We compare the performance of our algorithm against the previous state-of-the-art on synthetic and real-world datasets and show that our designed algorithm produces comparable or better HC trees with much lower running time.
APA
Laenen, S., Manghiuc, B.A. & Sun, H.. (2023). Nearly-Optimal Hierarchical Clustering for Well-Clustered Graphs. Proceedings of the 40th International Conference on Machine Learning, in Proceedings of Machine Learning Research 202:18207-18249 Available from https://proceedings.mlr.press/v202/laenen23a.html.

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