Methods of Hierarchical Clustering

TL;DR

This paper surveys various hierarchical clustering methods, including agglomerative algorithms, self-organizing maps, mixture models, and density-based approaches, highlighting recent advances like a linear-time algorithm.

Contribution

It provides a comprehensive overview of hierarchical clustering techniques and introduces a new efficient linear-time hierarchical clustering algorithm.

Findings

Efficient implementations of hierarchical clustering are available in R and other software.

Hierarchical density-based clustering methods are reviewed.

A new linear-time hierarchical clustering algorithm is described.

Abstract

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.