Research map / Learning theory and algorithms / Clustering
A Constant-Factor Bi-Criteria Approximation Guarantee for k-means++
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Oversample from k to βk centers, for constant β > 1 | D^ℓ sampling · k-clustering | expected approximation ratio: r_D^{(ℓ)} (1 + H_{k-1}) -> r_D^{(ℓ)} (1 + φ/(β-1)) | Helped |
| Use the derived expected-cost bound and Markov's inequality | D² sampling with ⌈16 (k + √k)⌉ centers · k-means | approximation factor at probability 0.03: 20 -> 9.137 | Helped |
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