Fairness in clustering refers to the requirement that certain respondent types like minority grouping have adequate representation across clusters to avoid bias. It has emerged as an actively researched area in last few years. Fairness can be a practical concern in market segmentation. I will briefly introduce fair clustering, then focus on comparing selected promising and practical algorithms on both benchmark and real data sets. I will discuss method choices, fairness specification, computation, and implementation.
