2026-08-07T04:57:34Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:12713 2024-03-06T10:50:39Z ddc:004 open_access

Hierarchical Clusterings of Unweighted Graphs Høgemo, Svein Paul, Christophe Telle, Jan Arne Hierarchical Clustering We study the complexity of finding an optimal hierarchical clustering of an unweighted similarity graph under the recently introduced Dasgupta objective function. We introduce a proof technique, called the normalization procedure, that takes any such clustering of a graph G and iteratively improves it until a desired target clustering of G is reached. We use this technique to show both a negative and a positive complexity result. Firstly, we show that in general the problem is NP-complete. Secondly, we consider min-well-behaved graphs, which are graphs H having the property that for any k the graph H^{(k)} being the join of k copies of H has an optimal hierarchical clustering that splits each copy of H in the same optimal way. To optimally cluster such a graph H^{(k)} we thus only need to optimally cluster the smaller graph H. Co-bipartite graphs are min-well-behaved, but otherwise they seem to be scarce. We use the normalization procedure to show that also the cycle on 6 vertices is min-well-behaved. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Svein Høgemo and Christophe Paul and Jan Arne Telle 2020 Is Part Of LIPIcs, Volume 170, 45th International Symposium on Mathematical Foundations of Computer Science (MFCS 2020) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.MFCS.2020.47 urn:nbn:de:0030-drops-127139 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.MFCS.2020.47 eng https://creativecommons.org/licenses/by/3.0/legalcode