A generic method is introduced to visualize in a “Gaussian-like way,” and onto ℝ 2 $\mathbb {R}^{2}$ , results of Gaussian or non-Gaussian–based clustering. The key point is to explicitly force a visualization based on a spherical Gaussian mixture to inherit from the within cluster overlap that is present in the initial clustering mixture. The result is a particularly user-friendly drawing of the clusters, providing any practitioner with an overview of the potentially complex clustering result. An entropic measure provides information about the quality of the drawn overlap compared with the true one in the initial space. The proposed method is illustrated on four real data sets of different types (categorical, mixed, functional, and network) and is implemented on the r package ClusVis .

中文翻译：

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高斯和非高斯聚类的基于高斯的可视化

引入了一种通用方法以“类高斯方式”可视化，并在 ℝ 2 $\mathbb {R}^{2}$ 上，高斯或非基于高斯的聚类结果。关键是明确地强制基于球形高斯混合的可视化继承自初始聚类混合中存在的聚类内重叠。结果是一个特别用户友好的聚类图，为任何从业者提供了潜在复杂聚类结果的概述。熵度量提供了有关绘制重叠与初始空间中真实重叠质量的信息。所提出的方法在四个不同类型（分类、混合、功能和网络）的真实数据集上进行了说明，并在 r 包 ClusVis 上实现。