Cluster analysis is a family of techniques that group a set of objects so that items in the same group, or cluster, are more similar to one another than to those in other groups. As an unsupervised method, it does not rely on predefined labels but instead discovers structure within data based on measures of similari…
Cluster analysis is a family of techniques that group a set of objects so that items in the same group, or cluster, are more similar to one another than to those in other groups. As an unsupervised method, it does not rely on predefined labels but instead discovers structure within data based on measures of similarity or distance. Cluster analysis is widely used in data mining, pattern recognition, image analysis, and market and scientific research to explore datasets, reveal hidden groupings, reduce complexity, and generate hypotheses. Model Based Research publishes peer-reviewed studies that develop and apply data-analytic and modelling methods, including clustering and related multivariate techniques. Reported work includes the clustering of objects for spatial data mining in a comparative study, and analyses that use grouping and dimensionality-reduction methods such as principal component analysis to identify subtypes and patterns in data. These studies illustrate how clustering and allied approaches are applied to organise and interpret complex datasets across different domains. This page gathers peer-reviewed, open-access research relevant to cluster analysis and the broader study of unsupervised learning and data mining methods.
9 peer-reviewed articles, ranked by relevance. Each links to its DOI.
The 9 articles above have been cited 83 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 Cluster Analysis, linking to each citing work.