Expanding the Class of Global Objective Functions for Dissimilarity-Based Hierarchical Clustering

Sebastien Roch ()
Additional contact information
Sebastien Roch: University of Wisconsin–Madison

Journal of Classification, 2023, vol. 40, issue 3, No 4, 513-526

Abstract: Abstract Recent work on dissimilarity-based hierarchical clustering has led to the introduction of global objective functions for this classical problem. Several standard approaches, such as average linkage clustering, as well as some new heuristics have been shown to provide approximation guarantees. Here, we introduce a broad new class of objective functions which satisfy desirable properties studied in prior work. Many common agglomerative and divisive clustering methods are shown to be greedy algorithms for these objectives, which are inspired by related concepts in phylogenetics.

Keywords: Hierarchical clustering; Greedy algorithms; Minimum evolution; 62H30; 68R01; 92D15 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00357-023-09447-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jclass:v:40:y:2023:i:3:d:10.1007_s00357-023-09447-x

Ordering information: This journal article can be ordered from
http://www.springer. ... hods/journal/357/PS2

DOI: 10.1007/s00357-023-09447-x

Access Statistics for this article

Journal of Classification is currently edited by Douglas Steinley

More articles in Journal of Classification from Springer, The Classification Society
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().