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Oral
Fair k-Center Clustering for Data Summarization
Matthäus Kleindessner · Pranjal Awasthi · Jamie Morgenstern

Thu Jun 13 11:40 AM -- 12:00 PM (PDT) @ Room 103
in Unsupervised Learning »

In data summarization we want to choose k prototypes in order to summarize a data set. We study a setting where the data set comprises several demographic groups and we are restricted to choose k_i prototypes belonging to group i. A common approach to the problem without the fairness constraint is to optimize a centroid-based clustering objective such as k-center. A natural extension then is to incorporate the fairness constraint into the clustering objective. Existing algorithms for doing so run in time super-quadratic in the size of the data set. This is in contrast to the standard k-center objective that can be approximately optimized in linear time. In this paper, we resolve this gap by providing a simple approximation algorithm for the k-center problem under the fairness constraint with running time linear in the size of the data set and k. If the number of demographic groups is small, the approximation guarantee of our algorithm only incurs a constant-factor overhead. We demonstrate the applicability of our algorithm on both synthetic and real data sets.

[ Oral] 

Author Information

Matthäus Kleindessner (Rutgers University)
Pranjal Awasthi (Rutgers University)
Jamie Morgenstern (Georgia Institute of Technology)

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