Research map / Learning theory and algorithms / Clustering
On the price of explainability for some clustering problems
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use Ex-Greedy instead of IMM | k-means explainable clustering · mice | cost ratio: 1,12 -> 1,08 | Helped |
| Use Ex-Greedy instead of IMM | k-means explainable clustering · anuran | cost ratio: 1,30 -> 1,15 | Helped |
| Use Ex-Greedy instead of IMM | k-means explainable clustering · CIFAR-10 | cost ratio: 1,23 -> 1,17 | Helped |
| Use Ex-Greedy instead of IMM | k-means explainable clustering · digits | cost ratio: 1,23 -> 1,24 | Hurt |
| Use Ex-Greedy instead of IMM | k-means explainable clustering · breast_cancer, iris, wine, covtype, avila, newgroup | cost ratio: 1,00 -> 1,00; 1,04 -> 1,04; 1,00 -> 1,00; 1,03 -> 1,03; 1,10 -> 1,10; 1,01 -> 1,01 | No clear effect |
| Constrain clustering to decision-tree-induced partitions | k-centers · k-centers | price of explainability: O\left( \sqrt{d}k^{1-1/d} \right) | Hurt |
| Constrain clustering to decision-tree-induced partitions | maximum-spacing clustering · maximum-spacing | price of explainability: \Theta(n-k) | Hurt |
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