
There is a necessary tradeoff between the speed and accuracy of compression algorithms for clustering big data. While fast, sublinear-time sampling methods can be sufficient for many practical datasets, optimal strong coresets are necessary to ensure robust compression guarantees across a wide range of data distributions.


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efficient-compression-strategies-for-clustering-big-data-balancing-speed-and-accuracy


Efficient Compression Strategies for Clustering Big Data: Balancing Speed and Accuracy


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The authors present a combinatorial algorithm that simultaneously approximates all ℓp-norms in correlation clustering, providing the first proof of minimal sacrifice needed to optimize different norms of the disagreement vector.


simultaneously-approximating-all-%E2%84%93p-norms-in-correlation-clustering


Simultaneously Approximating All ℓp-norms in Correlation Clustering
