Expanding the class of global objective functions for dissimilarity-based hierarchical clustering
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Abstract (arXiv)
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, 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.
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References · 7
- 2015A cost function for similarity-based hierarchical clusteringcited by 63
- 2016Approximate Hierarchical Clustering via Sparsest Cut and Spreading Metricscited by 26
- 2016Hierarchical Clustering via Spreading Metricscited by 17
- 2018Hierarchical Clustering better than Average-Linkagecited by 16
- 2018Hierarchical Clustering with Structural Constraintscited by 15
- 2020Hierarchical Clustering: a 0.585 Revenue Approximationcited by 8
- 2021Hierarchical Clustering: O(1)-Approximation for Well-Clustered Graphscited by 8
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