In the modal approach to clustering, clusters are defined as the local maxima of the underlying probability density function, where the latter can be estimated either nonparametrically or using finite mixture models. Thus, clusters are closely related to certain regions around the density modes, and every cluster corresponds to a bump of the density. The Modal Expectation-Maximization (MEM) algorithm is an iterative procedure that can identify the local maxima of any density function. In this contribution, we propose a fast and efficient MEM algorithm to be used when the density function is estimated through a finite mixture of Gaussian distributions with parsimonious component-covariance structures. After describing the procedure, we apply the proposed MEM algorithm on both simulated and real data examples, showing its high flexibility in several contexts.

中文翻译：

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一种快速高效的高斯混合模态 EM 算法

在聚类的模态方法中，聚类被定义为潜在概率密度函数的局部最大值，其中后者可以非参数地或使用有限混合模型进行估计。因此，簇与密度模式周围的某些区域密切相关，每个簇对应于密度的一个凸起。模态期望最大化 (MEM) 算法是一个迭代过程，可以识别任何密度函数的局部最大值。在这个贡献中，我们提出了一种快速有效的 MEM 算法，当通过具有简约分量协方差结构的高斯分布的有限混合估计密度函数时使用。在描述了该过程之后，我们将所提出的 MEM 算法应用于模拟和真实数据示例，