Abstract Similarity-based Hierarchical Clustering (HC) is a classical unsupervised machine
learning algorithm that has traditionally been solved with heuristic algorithms like Average …

Hierarchical clustering is a data analysis method that has been used for decades. Despite its
widespread use, the method has an underdeveloped analytical foundation. Having a well …

Hierarchical clustering is typically performed using algorithmic-based optimization searching
over the discrete space of trees. While these optimization methods are often effective, their …

Multilayer networks continue to gain significant attention in many areas of study, particularly
due to their high utility in modeling interdependent systems such as critical infrastructures …

Hierarchical clustering over graphs is a fundamental task in data mining and machine
learning with applications in many domains including phylogenetics, social network …

We consider the widely-used average-linkage, single-linkage, and Ward's methods for
computing hierarchical clusterings of high-dimensional Euclidean inputs. It is easy to show …

We initiate a comprehensive experimental study of objective-based hierarchical clustering
methods on massive datasets consisting of deep embedding vectors from computer vision …

Hierarchical Clustering trees have been widely accepted as a useful form of clustering data,
resulting in a prevalence of adopting fields including phylogenetics, image analysis …

Hierarchical clustering studies a recursive partition of a data set into clusters of successively
smaller size, and is a fundamental problem in data analysis. In this work we study the cost …

We investigate the problem of hierarchically clustering data streams containing metric data
in R^ d. We introduce a desirable invariance property for such algorithms, describe a …