Research map / Learning theory and algorithms / Clustering
Greedy Sampling for Approximate Clustering in the Presence of Outliers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use thresholded distance sampling (T-kmeans++) instead of k-means++ | k-means++ · NIPS (k=10,20,30) | outlier recall: 0.939 -> 0.973 (k=20) | Helped |
| use thresholded distance sampling (T-kmeans++) instead of k-means++ | k-means++ · Skin (k=10,20,30) | outlier recall: 0.642 -> 0.690 (k=20) | Helped |
| use thresholded distance sampling (T-kmeans++) instead of k-means++ | k-means++ · MNIST (k=10,20,30) | outlier recall: 0.982 -> 0.989 (k=20) | Helped |
| use thresholded distance sampling (T-kmeans++) instead of k-means++ | k-means++ · NIPS (k=10,20,30) | k-means objective: 4173211 -> 4167724 (k=10); 4046443 -> 4112852 (k=20) | Mixed |
| use thresholded distance sampling (T-kmeans++) instead of k-means++ | k-means++ · MNIST (k=10,20,30) | k-means objective: 1.546 ×108 -> 1.513 ×108 (k=10) | Helped |
| use thresholded distance sampling (T-kmeans++) instead of k-means++ | k-means++ · Skin (k=10,20,30) | k-means objective: 7726552 -> 7439527 (k=10) | Mixed |
| use far-point uniform sampling (Algorithm 1) instead of Gonzalez or random | Gonzalez · synthetic Gaussian mixture, k=20, z=100 | cluster recall: perfect recall at about k'=23 centers | Helped |
| use thresholded distance sampling (T-kmeans++) instead of k-means++ | k-means++ · synthetic, k=20, z=2000 | cluster recall | Helped |
Closest papers by meaning
- Near-Linear Time Approximation Algorithms for k-means with Outliers (ICML 2024 · similarity 0.91)
- Fast Algorithms for Distributed k-Clustering with Outliers (ICML 2023 · similarity 0.91)
- Clustering What Matters: Optimal Approximation for Clustering with Outliers (AAAI 2023 · similarity 0.90)
- A More Efficient Reduction from Outlier-Aware to Outlier-Free k-Median (AAAI 2026 · similarity 0.90)
- A Sampling-Based Approach for Efficient Clustering in Large Datasets (CVPR 2022 · similarity 0.89)
- Improved Guarantees for Fully Dynamic $k$-Center Clustering with Outliers in General Metric Spaces (NeurIPS 2024 · similarity 0.88)
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