We propose a hierarchical correlation clustering method that extends the well-known correlation clustering to produce hierarchical clusters. We then investigate embedding the respective hierarchy to be used for (tree preserving) embedding and feature extraction. We study the connection of such an embedding to single linkage embedding and minimax distances, and in particular study minimax distances for correlation clustering. Finally, we demonstrate the performance of our methods on several UCI and 20 newsgroup datasets.

翻译：我们提出一个等级相关分组方法,将众所周知的关联分组扩大至产生等级分组。然后我们调查各自等级的嵌入(树木保护)嵌入和特征提取。我们研究了这种嵌入与单一链接嵌入和最小距离的联系,特别是研究相关分组的最小距离。最后,我们展示了我们在若干 UCI 和 20 个新闻组数据集上的方法表现。