Expanding the class of global objective functions for dissimilarity-based hierarchical clustering
This work expands the theoretical framework for hierarchical clustering, potentially benefiting researchers in machine learning and data analysis, but it appears incremental as it builds on prior work without demonstrating broad SOTA gains.
The paper introduces a new class of global objective functions for dissimilarity-based hierarchical clustering, showing that many common agglomerative and divisive methods are greedy algorithms for these objectives, which are inspired by phylogenetics.
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.