Cluster analysis is a big, sprawling field. This review paper cannot hope to fully survey the
territory. Instead, it focuses on hierarchical agglomerative clustering, k‐means clustering …

Graph-based clustering has been considered as an effective kind of method in unsupervised
manner to partition various items into several groups, such as Spectral Clustering (SC) …

Multi-view subspace clustering achieves impressive performance for high-dimensional data.
However, many of these models do not sufficiently mine the intrinsic information among …

Recent advances in center-based clustering continue to improve upon the drawbacks of
Lloyd's celebrated $ k $-means algorithm over $60 $ years after its introduction. Various …

Clustering techniques have been instrumental in discerning patterns and relationships
within datasets in data analytics and unsupervised machine learning. Traditional clustering …

Clustering, a fundamental activity in unsupervised learning, is notoriously difficult when the
feature space is high-dimensional. Fortunately, in many realistic scenarios, only a handful of …

In unsupervised learning, clustering is a common starting point for data processing. The
convex or concave fusion clustering method is a novel approach that is more stable and …

Optimization is widely used in statistics, thanks to its efficiency for delivering point estimates
on useful spaces, such as those satisfying low cardinality or combinatorial structure. To …

Clustering is an essential technique that groups similar data points to uncover the
underlying structure and features of the data. Although traditional clustering methods such …

Common clustering methods, such as $ k $-means and convex clustering, group similar
vector-valued observations into clusters. However, with the increasing prevalence of matrix …