Research map / Learning theory and algorithms / Sample complexity
Almost Linear Time Density Level Set Estimation via DBSCAN
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace neighborhood k-nearest-neighbor search with grid-based core detection and LSH graph construction | DBSCAN · YouTube8M | running time: 5146.73 -> 56.69 seconds | Helped |
| Replace neighborhood k-nearest-neighbor search with grid-based core detection and LSH graph construction | DBSCAN · Synthetic, n = 106, D = 10 | running time: 118.46 -> 0.47 seconds | Helped |
| Replace neighborhood k-nearest-neighbor search with grid-based core detection and LSH graph construction | DBSCAN · Synthetic, n = 106, D = 100 | running time: 30643.61 -> 3.93 seconds | Helped |
| Replace neighborhood k-nearest-neighbor search with grid-based core detection and LSH graph construction | DBSCAN · Synthetic, n = 106, D = 300 | running time: >1day -> 11.66 seconds | Helped |
| Replace neighborhood k-nearest-neighbor search with grid-based core detection and LSH graph construction | DBSCAN · Synthetic, n = 107, D = 10 | running time: >1day -> 5.54 seconds | Helped |
| Replace neighborhood k-nearest-neighbor search with grid-based core detection and LSH graph construction | DBSCAN · Synthetic, n = 108, D = 10 | running time: >1day -> 48.58 seconds | Helped |
| Use the proposed approximation instead of DBSCAN or DBSCAN++ | Density-based clustering · Real-world datasets (A)-(K) | clustering quality | No clear effect |
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