论文标题
扩展基于差异的分层群集的全球目标函数类别
Expanding the class of global objective functions for dissimilarity-based hierarchical clustering
论文作者
论文摘要
关于基于差异的层次聚类的最新工作导致引入了这个经典问题的全球目标功能。已经证明了几种标准方法，例如平均链接以及一些新的启发式方法可提供近似保证。在这里，我们介绍了一系列广泛的目标功能，这些功能满足了先前研究中研究的理想属性。许多常见的聚集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。
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.
