Guarantees for Spectral Clustering with Fairness Constraints

24 Jan 2019Matthäus KleindessnerSamira SamadiPranjal AwasthiJamie Morgenstern

Given the widespread popularity of spectral clustering (SC) for partitioning graph data, we study a version of constrained SC in which we try to incorporate the fairness notion proposed by Chierichetti et al. (2017). According to this notion, a clustering is fair if every demographic group is approximately proportionally represented in each cluster... (read more)

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