Abstract Model-based clustering approaches generally assume that the observations to be clustered are generated from a mixture of distributions, each component of the mixture corresponding to a particular parametric distribution. Most commonly, the underlying distribution is assumed to be normal, which is inadequate for many situations, for example when skewness or multimodality is present within the components. The problem is intensified when the data dimension increases, leading to inaccurate groupings and incorrect inference. A new Bayesian model-based clustering approach is proposed, that can handle a variety of complexities in the data, based on a recently introduced family of geometric skew normal distributions. The performance of this methodology is illustrated through a number of simulation studies and applications to a number of datasets from genomics and medicine.

中文翻译：

---

使用几何偏态正态分布对偏态和多模态数据进行贝叶斯聚类

摘要 基于模型的聚类方法通常假设要聚类的观测值是从分布的混合生成的，混合的每个分量对应于特定的参数分布。最常见的是，基础分布被假定为正态分布，这在许多情况下是不够的，例如当分量中存在偏度或多峰性时。当数据维度增加时，问题会加剧，导致分组不准确和推理错误。基于最近引入的几何偏斜正态分布族，提出了一种新的基于贝叶斯模型的聚类方法，该方法可以处理数据中的各种复杂性。