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Found 3 papers, 1 papers with code
Randomized Greedy Algorithms and Composable Coreset for k-Center Clustering with Outliers
Though a number of methods have been developed in the past decades, it is still quite challenging to design quality guaranteed algorithm with low complexity for this problem.
The Effectiveness of Uniform Sampling for Center-Based Clustering with Outliers
The experiments suggest that the uniform sampling method can achieve comparable clustering results with other existing methods, but greatly reduce the running times.
Greedy Strategy Works for $k$-Center Clustering with Outliers and Coreset Construction
Our idea is inspired by the greedy method, Gonzalez's algorithm, for solving the problem of ordinary $k$-center clustering.
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