Recent work on dissimilarity-based hierarchical clustering has led to the introduction of global objective functions for this classical problem. Several standard approaches, such as average linkage, as well as some new heuristics have been shown to provide approximation guarantees. Here we introduce a broad new class of objective functions which satisfy desirable properties studied in prior work. Many common agglomerative and divisive clustering methods are shown to be greedy algorithms for these objectives, which are inspired by related concepts in phylogenetics.

中文翻译：

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扩展基于差异的层次聚类的全局目标函数类

最近关于基于差异的层次聚类的工作导致为这个经典问题引入全局目标函数。几种标准方法，例如平均链接，以及一些新的启发式方法已被证明可以提供近似保证。在这里，我们介绍了一类广泛的新目标函数，它们满足先前工作中研究的理想特性。许多常见的凝聚和分裂聚类方法被证明是这些目标的贪婪算法，这些算法受到系统发育学中相关概念的启发。