This paper develops a new method for hierarchical clustering. Unlike other existing
clustering schemes, our method is based on a generative, tree-structured model that …

This paper presents a new method for solving clustering problem. We treat clustering as a
graph-partitioning problem and propose a new global criterion, the optimum cut, for …

This paper presents a generic method for perceptual grouping and an analysis of its
expected grouping quality. The grouping method is fairly general: It may be used for the …

Numerous clustering algorithms, their taxonomies and evaluation studies are available in
the literature. Despite the diversity of different clustering algorithms, solutions delivered by …

This chapter provides a tutorial overview of hierarchical clustering. Several data
visualization methods based on hierarchical clustering are demonstrated and the scaling of …

A methodology for clustering data in which a distance metric or similarity function is not used
is described. Instead, clusterings are optimized based on their intended function: the …

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 linkage …

This paper addresses the problem of cluster defining criteria by proposing a model-based
characterization of interpattern relationships. Taking a dissimilarity matrix between patterns …

In this work we propose a new information-theoretic clustering algorithm that infers cluster
memberships by direct optimization of a non-parametric mutual information estimate …

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 …