Initialisation of the EM algorithm in model-based clustering is often crucial. Various starting points in the parameter space often lead to different local maxima of the likelihood function and, so to different clustering partitions. Among the several approaches available in the literature, model-based agglomerative hierarchical clustering is used to provide initial partitions in the popular mclust R package. This choice is computationally convenient and often yields good clustering partitions. However, in certain circumstances, poor initial partitions may cause the EM algorithm to converge to a local maximum of the likelihood function. We propose several simple and fast refinements based on data transformations and illustrate them through data examples.

中文翻译：

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使用高斯分层分区改进了基于模型的聚类的初始化。

在基于模型的聚类中，EM算法的初始化通常至关重要。参数空间中的各种起点通常会导致似然函数的局部极大值不同，从而导致不同的聚类分区。在文献中可用的几种方法中，基于模型的聚集层次聚类用于提供流行的mclust R包中的初始分区。该选择在计算上很方便，并且通常会产生良好的聚类分区。但是，在某些情况下，不良的初始分区可能导致EM算法收敛到似然函数的局部最大值。我们提出了一些基于数据转换的简单快速改进方法，并通过数据示例进行说明。