In this work, we propose a robust cluster analysis methodology based on cellwise trimming as an extension to a robust version of Principal Component Analysis. This new approach is more reasonable than traditional casewise trimming when the dimension is not small. This type of trimming avoids an unnecessary loss of information when only a few cells of the entirely trimmed observations are atypical. We propose an algorithm to apply this approach. This algorithm is particularized to the case of functional cluster analysis. We provide simulations and applications using real data sets to illustrate the proposed methodology.

中文翻译：

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具有单元格修整的聚类分析和用于曲线稳健聚类的应用

在这项工作中，我们提出了一种基于单元格修剪的稳健聚类分析方法，作为主成分分析稳健版本的扩展。当维度不小时，这种新方法比传统的casewise修整更合理。当完全修剪的观察值中只有少数单元格是非典型的时，这种类型的修剪避免了不必要的信息丢失。我们提出了一种算法来应用这种方法。该算法专门用于功能聚类分析的情况。我们提供使用真实数据集的模拟和应用程序来说明所提出的方法。