Research map / Learning theory and algorithms / Clustering
Clustering High Dimensional Dynamic Data Streams
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use a randomly shifted nested grid and telescope-sum cell centers to construct the coreset | Dynamic k-median on [∆]^d · Dynamic k-median on [∆]^d | space dependence on dimension | Helped |
| Use a cutoff telescope sum and sample only points from heavy cells to obtain non-negative weights | Dynamic k-median coreset construction · Dynamic k-median on [∆]^d | weight sign | Helped |
| Represent the Gaussian mixture dataset with a 90-point coreset | Offline coreset construction · Gaussian mixture data in R2 | maximum relative error: under 10% | Helped |
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