A model-based clustering method based on Gaussian Cox process is proposed to address the problem of clustering of count process data. The model allows for nonparametric estimation of intensity functions of Poisson processes, while simultaneous clustering count process observations. A logistic Gaussian process transformation is imposed on the intensity functions to enforce smoothness. Maximum likelihood parameter estimation is carried out via the EM algorithm, while model selection is addressed using a cross-validated likelihood approach. The proposed model and methodology are applied to two datasets.

中文翻译：

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基于模型的计数过程聚类

针对计数过程数据的聚类问题，提出了一种基于高斯Cox过程的基于模型的聚类方法。该模型允许对泊松过程的强度函数进行非参数估计，同时对过程观察进行聚类计数。对强度函数施加逻辑高斯过程变换以增强平滑度。最大似然参数估计是通过 EM 算法进行的，而模型选择是使用交叉验证的似然方法解决的。所提出的模型和方法适用于两个数据集。