We study hierarchical clustering schemes under an axiomatic view. We show that within this framework, one can prove a theorem analogous to one of Kleinberg (2002), 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.clustering; hierarchical clustering; stability of clustering; Gromov-Hausdorff distanc
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
We present an axiomatic construction of hierarchical clustering in asymmetric networks where the dis...
This paper explores hierarchical clustering in the case where pairs of points have dissimilarity sco...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
We present a multiscale, consistent approach to density-based clustering that satisfies stability th...
Many clustering schemes are defined by optimizing an objective function defined on the partitions of...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
We propose an extension of hierarchical clustering methods, called multiparameter hierarchical clust...
AbstractWe show that for any data set in any metric space, it is possible to construct a hierarchica...
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
Abstract We formulate and (approximately) solve hierarchical versions of two prototypical problems i...
Several methods in data and shape analysis can be regarded as transformations between metric spaces....
We propose a theoretically and practically improved density-based, hierarchical clustering method, p...
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
We present an axiomatic construction of hierarchical clustering in asymmetric networks where the dis...
This paper explores hierarchical clustering in the case where pairs of points have dissimilarity sco...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
We present a multiscale, consistent approach to density-based clustering that satisfies stability th...
Many clustering schemes are defined by optimizing an objective function defined on the partitions of...
Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly fi...
We propose an extension of hierarchical clustering methods, called multiparameter hierarchical clust...
AbstractWe show that for any data set in any metric space, it is possible to construct a hierarchica...
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
Abstract We formulate and (approximately) solve hierarchical versions of two prototypical problems i...
Several methods in data and shape analysis can be regarded as transformations between metric spaces....
We propose a theoretically and practically improved density-based, hierarchical clustering method, p...
Hierarchical clustering is typically implemented as a greedy heuristic algorithm with no explicit ob...
We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations tha...
We present an axiomatic construction of hierarchical clustering in asymmetric networks where the dis...