Research map / Learning theory and algorithms / Clustering
A Practical Algorithm for Distributed Clustering and Outlier Detection
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use ball-grow summaries instead of k-means|| summaries | distributed clustering · gauss-0.1 | ℓ2-loss: 5.40e+4 -> 4.80e+4 | Helped |
| Use ball-grow summaries instead of k-means|| summaries | distributed clustering · gauss-0.1 | recall: 0.6235 -> 0.9431 | Helped |
| Use ball-grow summaries instead of k-means|| summaries | distributed clustering · gauss-0.4 | ℓ2-loss: 2.79e+5 -> 2.72e+5 | Helped |
| Use ball-grow summaries instead of k-means|| summaries | distributed clustering · gauss-0.4 | recall: 0.2458 -> 0.7657 | Helped |
| Use ball-grow summaries instead of k-means|| summaries | distributed clustering · kddSp | ℓ2-loss: 4.19e+6 -> 3.46e+6 | Helped |
| Use ball-grow summaries instead of k-means++ summaries | distributed clustering · kddSp | recall: 0.1787 -> 0.5176 | Helped |
| Use ball-grow summaries instead of k-means++ summaries | distributed clustering · kddFull | ℓ2-loss: 4.65e+7 -> 3.54e+7 | Helped |
| Use ball-grow summaries instead of k-means++ summaries | distributed clustering · kddFull | recall: 0.1439 -> 0.5803 | Helped |
| Use ball-grow summaries instead of k-means|| summaries | distributed clustering · susy-5 | recall: 0.1609 -> 0.5933 | Helped |
| Use ball-grow summaries instead of k-means++ summaries | distributed clustering · Spatial-15 | recall: 0.7697 -> 0.9993 | Helped |
| Use ball-grow summaries instead of k-means++ summaries | distributed clustering · kddFull | ℓ1-loss: 8.21e+6 -> 7.38e+6 | Helped |
| Use ball-grow summaries instead of k-means++ summaries | distributed clustering · Spatial-15 | ℓ2-loss: 7.79e+5 -> 7.19e+5 | Helped |
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- Fast Algorithms for Distributed k-Clustering with Outliers (ICML 2023 · similarity 0.92)
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- A More Efficient Reduction from Outlier-Aware to Outlier-Free k-Median (AAAI 2026 · similarity 0.89)
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