We propose a model-based clustering procedure where each component can take into account cluster-specific mild outliers through a flexible distributional assumption, and a proportion of observations is additionally trimmed. We propose a penalized likelihood approach for estimation and selection of the proportions of mild and gross outliers. A theoretically grounded penalty parameter is then obtained. Simulation studies illustrate the advantages of our procedure over flexible mixtures without trimming, and over trimmed normal mixture models (tclust). We conclude with an original real data example on the identification of the source from illicit drug shipments seized in Italy and Spain. The methodology proposed in this paper has been implemented in R functions which can be downloaded from https://github.com/afarcome/cntclust.

我们提出了一种基于模型的聚类程序，其中每个组件都可以通过灵活的分布假设考虑到特定于聚类的轻度离群值，并且还对一部分观察值进行了修剪。我们提出了一种惩罚似然法，用于估计和选择轻度和严重异常值的比例。然后获得理论上的惩罚参数。仿真研究证明了我们的程序比不进行修整的柔性混合物和经过修整的普通混合物模型（tclust）的优势。我们以一个原始的真实数据示例作为结尾，该示例说明了在意大利和西班牙查获的非法药物装运来源的识别。本文提出的方法已在R中实现 可以从https://github.com/afarcome/cntclust下载的功能。