Characterization, Stability and Convergence of Hierarchical Clustering Methods 论文
2010引用 227
Topological and Geometric Data AnalysisData Management and AlgorithmsAdvanced Clustering Algorithms Research
摘要
Abstract. We study hierarchical clustering schemes under an axiomatic view. We show that within this framework, one can prove a theorem analogous to one of J. Kleinberg [8], in which one obtains an existence and uniqueness theorem instead of a non-existence result. We explore further properties of this unique scheme: stability and convergence are established. We represent dendrograms as ultrametric spaces and use tools from metric geometry, namely the Gromov-Hausdorff distance, to quantify the degree to which perturbations in the input metric space affect the result of hierarchical methods. Contents