We are currently witnessing a paradigm shift from evidence-based medicine to precision medicine, which has been made possible by the enormous development of technology. The advances in data mining algorithms will allow us to integrate trans-omics with clinical data, contributing to our understanding of pathological mechanisms and massively impacting on the clinical sciences. Cluster analysis is one of the main data mining techniques and allows for the exploration of data patterns that the human mind cannot capture. This chapter focuses on the cluster analysis of clinical data, using the statistical software, R. We outline the cluster analysis process, underlining some clinical data characteristics. Starting with the data preprocessing step, we then discuss the advantages and disadvantages of the most commonly used clustering algorithms and point to examples of their applications in clinical work. Finally, we briefly discuss how to perform validation of clusters. Throughout the chapter we highlight R packages suitable for each computational step of cluster analysis.

Original languageEnglish
Title of host publicationMethods in Molecular Biology
PublisherHumana Press
Number of pages35
Publication statusPublished - 2020

Publication series

NameMethods in Molecular Biology
ISSN (Print)1064-3745
ISSN (Electronic)1940-6029


  • Clinical data
  • Cluster analysis
  • Cluster optimization
  • Cluster stability
  • Cluster tendency
  • Cluster validation
  • Stratification


Dive into the research topics of 'Clustering clinical data in R'. Together they form a unique fingerprint.

Cite this