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

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  • Sebastien Roch

    (University of Wisconsin–Madison)

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.

Suggested Citation

  • Sebastien Roch, 2023. "Expanding the Class of Global Objective Functions for Dissimilarity-Based Hierarchical Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(3), pages 513-526, November.
    • Handle: RePEc:spr:jclass:v:40:y:2023:i:3:d:10.1007_s00357-023-09447-x
    DOI: 10.1007/s00357-023-09447-x
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