Algorithms for clustering data
Algorithms for clustering data
A uniqueness theorem for clustering
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A sober look at clustering stability
COLT'06 Proceedings of the 19th annual conference on Learning Theory
Metric structures on datasets: stability and classification of algorithms
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Computing the shape of brain networks using graph filtration and gromov-hausdorff metric
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part II
Remote sensing image segmentation by active queries
Pattern Recognition
Generation of coalition structures to provide proper groups' formation in group recommender systems
Proceedings of the 22nd international conference on World Wide Web companion
Stability of density-based clustering
The Journal of Machine Learning Research
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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.