A First Tutorial on Cluster Analysis

Welcome

This page includes some basic material on cluster analysis (clustering).

The materials presented were created by Efthymios Costa and they serve as an introduction to dissimilarity-based clustering, involving partitional and hierarchical clustering of continuous data. The intended reader is an undergraduate student who is familiar with basic concepts of statistics and analysis.

Clustering is, roughly speaking, the task of detecting group structures in data sets. It has been used for several applications, such as customer segmentation, gene identification, fraud detection, or document classification, among others. The goal of these materials is to familiarise the reader with some basic clustering concepts and allow them to apply these on some data set that is of interest to them.

Content

We will have several sessions/workshops. These will be focusing on different aspects of cluster analysis.

  1. Introduction: What is clustering?
  2. Distances & Dissimilarities
  3. Partitional Clustering
  4. Hierarchical Clustering
  5. Number Of Clusters & Evaluation

Some ideas on possible projects are included in Project Ideas, together with links to two data repositories.

Software

The tutorials will make use of the R programming language (R Core Team, 2025) and it is recommended that certain packages are installed in advance. Make sure you have downloaded R and RStudio, then go to the Console of RStudio and paste the following:

required_pkgs <- c("aricode",
                   "cluster",
                   "factoextra",
                   "GGally",
                   "ggplot2",
                   "patchwork")
install.packages(required_pkgs)

Notice that this may take a bit of time. Additional packages can be used if you wish to use specific functions not available in the packages listed above.

Reading List

There are several textbooks available that include the materials covered here and much more about clustering. The following provide excellent starting points for exploring clustering in more depth:

Kaufman L, Rousseeuw P.J. (1990). Finding Groups in Data: an Introduction to Cluster Analysis. John Wiley & Sons

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning with applications in R [Chapter 12. Unsupervised Learning].

Hennig, C., Meila, M., Murtagh, F., & Rocci, R. (Eds.). (2015). Handbook of cluster analysis. CRC press.

Citation

If you use these resources in your work, please cite as follows:

Costa, E. (2026). A First Tutorial on Cluster Analysis [Course materials]. https://efthymioscosta.github.io/m1r.ec1917/.

@online{costa2026clustering,
  author = {Efthymios Costa},
  title = {A {F}irst {T}utorial on {C}luster {A}nalysis},
  year = {2026},
  note = {[Course materials]},
  url = {https://efthymioscosta.github.io/m1r.ec1917/}
}

License

The content of this website is published under the Creative Commons Attribution 4.0 International license. This license lets you distribute, remix, adapt, and build upon this work, even commercially, on the condition that you give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.

About this Website

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References

R Core Team. (2025). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/