Research Topic · Peer-Reviewed

Hierarchical Clustering

Hierarchical clustering is an unsupervised machine-learning method that organises data points into a nested hierarchy of clusters based on a chosen measure of similarity or distance. It comes in two forms: agglomerative clustering, which begins with each observation as its own cluster and successively merges the clo…

Curated from this journal's research 📚 8 peer-reviewed articles cited Cited 52× across the literature 🔖 ISSN 2768-0207 🗓 Reviewed July 2026

Overview

Hierarchical clustering is an unsupervised machine-learning method that organises data points into a nested hierarchy of clusters based on a chosen measure of similarity or distance. It comes in two forms: agglomerative clustering, which begins with each observation as its own cluster and successively merges the closest pairs, and divisive clustering, which begins with all observations together and recursively splits them. The result is typically represented as a dendrogram, a tree diagram whose branching structure records the order and distance at which clusters are joined or divided, allowing the analyst to choose a level at which to cut the tree and obtain a particular partition. Key design choices include the distance metric and the linkage criterion, such as single, complete, average, or Ward linkage, which define how the dissimilarity between groups is computed and strongly influence the shape of the resulting clusters. Because it requires no prior specification of the number of clusters and reveals multi-scale structure, hierarchical clustering is widely used in exploratory data analysis, data mining, bioinformatics, and pattern recognition across fields. Related applications include clustering objects for spatial data mining, constructing phylogenetic and similarity trees from morphometric or molecular traits, and comparing grouping strategies alongside other multivariate techniques such as principal component analysis, illustrating its role in discovering natural groupings within complex, high-dimensional datasets.

Research published in this journal

8 peer-reviewed articles, ranked by relevance. Each links to its DOI.

How this research is being cited

The 8 articles above have been cited 52 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.

A sample of recent works citing this journal's research on Hierarchical Clustering, linking to each citing work.

Editorial oversight

Curated from peer-reviewed research published in Big Data Research (ISSN 2768-0207).

Journal editorial board
Professor Shangming Zhou · United Kingdom Professor Hong Lin · United States Dr. Rami H. Al-Rifai · United Arab Emirates

This page summarises published research for orientation; it is not medical or professional advice.