Hierarchical Clustering With Prototypes via Minimax Linkage

Hierarchical Clustering With Prototypes via Minimax Linkage.

Agglomerative hierarchical clustering is a popular class of methods for understanding the structure of a dataset. The nature of the clustering depends on the choice of linkage-that is, on how one measures the distance between clusters. In this article we investigate minimax linkage, a recently introduced but little-studied linkage. Minimax linkage is unique in naturally associating a prototype ...

Poisoning Complete-Linkage Hierarchical Clustering

Clustering algorithms are largely adopted in security applications as a vehicle to detect malicious activities, although few attention has been paid on preventing deliberate attacks from subverting the clustering process itself. Recent work has introduced a methodology for the security analysis of data clustering in adversarial settings, aimed to identify potential attacks against clustering al...

Clustering with Repulsive Prototypes

A Characterization of Linkage-Based Hierarchical Clustering

The class of linkage-based algorithms is perhaps the most popular class of hierarchical algorithms. We identify two properties of hierarchical algorithms, and prove that linkagebased algorithms are the only ones that satisfy both of these properties. Our characterization clearly delineates the difference between linkage-based algorithms and other hierarchical methods. We formulate an intuitive ...

Hierarchical Clustering via Spreading Metrics

We study the cost function for hierarchical clusterings introduced by [Dasgupta, 2016] where hierarchies are treated as first-class objects rather than deriving their cost from projections into flat clusters. It was also shown in [Dasgupta, 2016] that a top-down algorithm returns a hierarchical clustering of cost at most O (αn log n) times the cost of the optimal hierarchical clustering, where ...