Change-point detection for categorical data has wide applications in many fields. Existing methods either are distribution-free, not utilizing categorical information sufficiently, or have limited performance when there exists “rare events” (events that occur with low frequency). In this paper, we propose a Bayesian change-point detection model for categorical data based on Dirichlet-multinomial mixtures. Because of the introduction of prior information, our method performs well for the existence of “rare events”. An online parameter estimation procedure and an online detection strategy are then designed to adapt to data streams. Monte Carlo simulations discuss the power of the proposed method and show advantages compared with existing algorithms. Applications in biomedical research, document analysis, health news case study and location monitoring indicate practical values of our method.

分类数据的变更点检测在许多领域都有广泛的应用。现有方法要么没有发行权，没有充分利用分类信息，要么在存在“罕见事件”（频率较低的事件）时性能有限。在本文中，我们提出了一种基于Dirichlet-多项式混合的贝叶斯分类数据的贝叶斯变化点检测模型。由于引入了先验信息，因此我们的方法对于“罕见事件”的存在表现良好。然后设计一种在线参数估计程序和一种在线检测策略以适应数据流。蒙特卡洛仿真讨论了该方法的功能，并显示了与现有算法相比的优势。在生物医学研究，文档分析，