Research map / Learning theory and algorithms / Clustering
Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the proposed Spark implementation instead of sequential Prim's algorithm | ℓ2 single-linkage clustering · Image, KDDCUP04Bio, Shuttle, and US Census datasets | runtime speedup: more than an order of magnitude | Helped |
| Use the proposed Spark implementation instead of sequential Prim's algorithm | ℓ2 single-linkage clustering · SIFT10M and HIGGS | runtime: 1.2\times 10^5 s; 8.4 \times 10^4 s | Mixed |
| Increase the approximation parameter η | Spark implementation of ℓ2 single-linkage clustering · KDDCUP04Bio and US Census | runtime speedup | Helped |
| Use the partition-based approximate MST approach | ℓ1, ℓ2, and ℓ∞ single-linkage clustering · Constant-dimensional vector data | MPC rounds: O(log n) | Helped |
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