Hierarchical Quasi-Clustering Methods for Asymmetric Networks
Hierarchical Quasi-Clustering Methods for Asymmetric Networks
复制标题
非对称网络的分层准聚类方法
DOI：
--
复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Santiago Segarra
中科院分区：
文献类型：
--
作者：
G. Carlsson;F. Mémoli;Alejandro Ribeiro;Santiago Segarra
本文介绍了层次准聚类方法，一个推广的层次聚类的非对称网络的输出结构保持输入数据的不对称性。我们证明了这种输出结构等价于一个有限的准超度量空间，并研究了关于两个理想性质的容许性。我们证明了一个修改后的版本的单链接是唯一允许的准聚类方法。此外，我们证明了所提出的方法的稳定性，我们建立的不变性履行it.Algorithms进一步发展和准聚类分析的价值与美国境内的移民研究。
This paper introduces hierarchical quasi-clustering methods, a generalization of hierarchical clustering for asymmetric networks where the output structure preserves the asymmetry of the input data. We show that this output structure is equivalent to a finite quasi-ultrametric space and study admissibility with respect to two desirable properties. We prove that a modified version of single linkage is the only admissible quasi-clustering method. Moreover, we show stability of the proposed method and we establish invariance properties fulfilled by it. Algorithms are further developed and the value of quasi-clustering analysis is illustrated with a study of internal migration within United States.