Clustering is a general term for the set of techniques that, given a set of objects, aim to select
those that are closer to one another than to the rest of the objects, according to a chosen …

The clustering of data into physically meaningful subsets often requires assumptions
regarding the number, size, or shape of the subgroups. Here, we present a new method …

Background Clustering is a widely used collection of unsupervised learning techniques for
identifying natural classes within a data set. It is often used in bioinformatics to infer …

Clustering is one of the most universal approaches for understanding complex data. A
pivotal aspect of clustering analysis is quantitatively comparing clusterings; clustering …

When some 'entities' are related by the 'features' they share they are amenable to a bipartite
network representation. Plant-pollinator ecological communities, co-authorship of scientific …

We review clustering as an analysis tool and the underlying concepts from an introductory
perspective. What is clustering and how can clusterings be realised programmatically? How …

Dimensionality reduction and clustering techniques are frequently used to analyze complex
data sets, but their results are often not easy to interpret. We consider how to support users …

Selective clustering annotated using modes of projections (SCAMP) is a new clustering
algorithm for data in $\mathbb {R}^ p $. SCAMP is motivated from the point of view of non …

In an age of increasingly large data sets, investigators in many different disciplines have
turned to clustering as a tool for data analysis and exploration. Existing clustering methods …