公平贝叶斯基于模型的聚类
Fair Bayesian Model-Based Clustering
AI总结：
针对现有群体公平聚类多依赖K-means、需预定义聚类数和距离的局限，提出公平贝叶斯聚类FBC，通过特殊公平先验和高效MCMC算法，可推断聚类数、适配多数据类型，在真实数据集上实现有竞争力的效用-公平性权衡。
摘要：
随着机器学习技术的发展以及对可信AI需求的增长，公平聚类已成为一项具有社会意义的重要任务。群体公平性要求每个敏感群体在所有聚类中的占比相近。现有大多数群体公平聚类方法都基于$K$-means聚类，因此需要预先给定实例间的距离以及聚类数量。为解决这一局限，我们提出一种公平贝叶斯基于模型的聚类方法，名为Fair Bayesian Clustering（FBC，公平贝叶斯聚类）。我们设计了一种特殊的先验，其概率质量仅分布在公平聚类上，并实现了一种高效的MCMC算法。FBC的优势在于它能够推断聚类数量，并且只要定义了似然函数就可应用于任意数据类型（例如分类数据）。在真实世界数据集上的实验表明，FBC（i）能合理推断聚类数量，（ii）与现有公平聚类方法相比，在效用-公平性权衡上具备竞争力，（iii）在分类数据上表现良好。
英文摘要：
Fair clustering has become a socially significant task with the advancement of machine learning technologies and the growing demand for trustworthy AI. Group fairness ensures that the proportions of each sensitive group are similar in all clusters. Most existing group-fair clustering methods are based on the $K$-means clustering and thus require the distance between instances and the number of clusters to be given in advance. To resolve this limitation, we propose a fair Bayesian model-based clustering called Fair Bayesian Clustering (FBC). We develop a specially designed prior which puts its mass only on fair clusters, and implement an efficient MCMC algorithm. Advantages of FBC are that it can infer the number of clusters and can be applied to any data type as long as the likelihood is defined (e.g., categorical data). Experiments on real-world datasets show that FBC (i) reasonably infers the number of clusters, (ii) achieves a competitive utility-fairness trade-off compared to existing fair clustering methods, and (iii) performs well on categorical data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
