Fair Model-based Clustering
This addresses scalability and data type limitations in fair clustering for applications requiring large datasets or categorical data, though it is incremental as it builds on existing fair clustering methods.
The paper tackles the scalability issue in fair clustering by proposing Fair Model-based Clustering (FMC), which reduces the number of learnable parameters to be independent of sample size, enabling mini-batch learning and handling non-metric data, with theoretical and empirical justifications provided.
The goal of fair clustering is to find clusters such that the proportion of sensitive attributes (e.g., gender, race, etc.) in each cluster is similar to that of the entire dataset. Various fair clustering algorithms have been proposed that modify standard K-means clustering to satisfy a given fairness constraint. A critical limitation of several existing fair clustering algorithms is that the number of parameters to be learned is proportional to the sample size because the cluster assignment of each datum should be optimized simultaneously with the cluster center, and thus scaling up the algorithms is difficult. In this paper, we propose a new fair clustering algorithm based on a finite mixture model, called Fair Model-based Clustering (FMC). A main advantage of FMC is that the number of learnable parameters is independent of the sample size and thus can be scaled up easily. In particular, mini-batch learning is possible to obtain clusters that are approximately fair. Moreover, FMC can be applied to non-metric data (e.g., categorical data) as long as the likelihood is well-defined. Theoretical and empirical justifications for the superiority of the proposed algorithm are provided.