Research map / Learning theory and algorithms / Clustering
Query Complexity of Clustering with Side Information
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add a similarity matrix whose within-cluster and between-cluster values follow f_+ and f_- | Query-Cluster · Query-Cluster | query complexity: Theta(nk) -> O(k^2 log n / H^2(f_+||f_-)) | Helped |
| Use a Las Vegas algorithm instead of the Monte Carlo algorithm to guarantee recovery with probability 1 | Query-Cluster with side information · Query-Cluster | query complexity: O(min(nk, k^2 log n / H^2(f_+||f_-))) -> O(n log n + min(nk, k^2 log n / H^2(f_+||f_-))) | Mixed |
| Allow side information to guide clustering queries | Query-Cluster · Query-Cluster | query complexity: Theta(nk) -> O(k^2 log n / H^2(f_+||f_-)) | Helped |
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