Research map / Learning theory and algorithms / Clustering
Hierarchical Agglomerative Clustering via Relaxed Representatives
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Proposes a relaxed-representative approach to hierarchical agglomerative clustering that aims to keep the interpretability of centroid/medoid linkage while reducing their computational cost.
Problem
Representative-based linkage methods for hierarchical agglomerative clustering (centroid, medoid) give interpretable cluster summaries but suffer from high computational cost and other drawbacks.
Approach
Relaxes the requirement that cluster representatives be actual data points or exact centroids, using relaxed representatives to make representative-based linkage cheaper while retaining intuitive cluster summaries. Only the abstract was available, so details are limited.
Limitations
- Full text unavailable; card based only on abstract
Experimental setup
Builds on: centroid linkage medoid linkage
Closest papers by meaning
- Efficient Centroid-Linkage Clustering (NeurIPS 2024 · similarity 0.91)
- On the cohesion and separability of average-link for hierarchical agglomerative clustering (NeurIPS 2024 · similarity 0.90)
- New Bounds on the Cohesion of Complete-link and Other Linkage Methods for Agglomerative Clustering (ICML 2024 · similarity 0.89)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.87)
- Hierarchical Overlapping Clustering on Graphs: Cost Function, Algorithm and Scalability (ICML 2025 · similarity 0.87)
- From Logits to Hierarchies: Hierarchical Clustering made Simple (ICML 2025 · similarity 0.86)
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