Reviews: Distributed k -Clustering for Data with Heavy Noise

The paper describes algorithms for distributed k-center/k-median/k-means clustering. For distributed k-center, it is known that any constant approximation needs Omega(z) communication cost, where z is the number of allowed outliers. This can be avoided by using a bicriteria approximation where more than z outliers are allowed, but the cost is compared to the best solution with at most z outliers. It is known that using 2z outliers is sufficient to obtain a O(1)-approximation. The paper at hand now improves the violation with respect to the number of outliers to (1 eps)*z.