Research map / Learning theory and algorithms / Clustering
Explainable k-Means and k-Medians Clustering
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use one feature threshold to produce two clusters | 2-medians · 2-medians | approximation factor: 2; lower bound 2 - \frac{1}{d} | Mixed |
| Use one feature threshold to produce two clusters | 2-means · 2-means | approximation factor: 4; lower bound 3\left(1 - \frac{1}{d} \right)^2 | Mixed |
| Build a k-leaf threshold tree by minimizing mistakes at each split | IMM with reference centers · k-medians, k > 2 | approximation factor: O(k) | Helped |
| Build a k-leaf threshold tree by minimizing mistakes at each split | IMM with reference centers · k-means, k > 2 | approximation factor: O(k^2) | Helped |
| Constrain clustering to a threshold tree with k leaves | k-medians · Constructed hard dataset, k >= 2 | approximation factor: \Omega(\log k) | Hurt |
| Constrain clustering to a threshold tree with k leaves | k-means · Constructed hard dataset, k >= 2 | approximation factor: \Omega(\log k) | Hurt |
| Use entropy-based greedy splits rather than a clustering-cost-aware split | ID3 · 3-means motivating example | clustering cost: \Omega(v) \to \infty | Hurt |
Closest papers by meaning
- Near-Optimal Algorithms for Explainable k-Medians and k-Means (ICML 2021 · similarity 0.93)
- Dynamic Algorithm for Explainable $k$-medians Clustering under $\ell_p$ Norm (NeurIPS 2025 · similarity 0.92)
- Explaining Kernel Clustering via Decision Trees (ICLR 2024 · similarity 0.90)
- Average Sensitivity of Hierarchical $k$-Median Clustering (ICML 2025 · similarity 0.89)
- Ultrametric Cluster Hierarchies: I Want ‘em All! (NeurIPS 2025 · similarity 0.88)
- SpEx: A Spectral Approach to Explainable Clustering (NeurIPS 2025 · similarity 0.87)
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