Source code - Fast and Accurate Fair k-Center Clustering

We study the classic 𝑘-center clustering problem under the additional constraint that each cluster should be fair. In this setting,
each point is marked with one or more colors, which can be used to
model protected attributes (e.g., gender or ethnicity). A cluster is
deemed fair if, for every color, the fraction of its points marked with
that color is within some prespecified range. We present a coreset-based approach to fair 𝑘-center clustering for general metric spaces
which attains almost the best approximation quality of the current
state of the art solutions, while featuring running times which can
be orders of magnitude faster for large datasets of low doubling
dimension. We devise sequential, streaming and MapReduce imple-
mentations of our approach and conduct a thourough experimental
analysis to provide evidence of their practicality, scalability, and
effectiveness.

The paper describing this research is available here: https://doi.org/10.1145/3589334.3645568