English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Convex clustering is an attractive clustering algorithm with favorableproperties such as efficiency and optimality owing to its convex formulation.It is thought to generalize both k-means clustering and agglomerativeclustering. However, it is not known whether convex clustering preservesdesirable properties of these algorithms. A common expectation is that convexclustering may learn difficult cluster types such as non-convex ones. Currentunderstanding of convex clustering is limited to only consistency results onwell-separated clusters. We show new understanding of its solutions. We provethat convex clustering can only learn convex clusters. We then show that theclusters have disjoint bounding balls with significant gaps. We furthercharacterize the solutions, regularization hyperparameters, inclusterable casesand consistency.

On Convex Clustering Solutions