Summarising and comparing clustering results
Abstract
This chapter shows how clustering results can be summarised, irrespective of the algorithm used for the analysis. It starts by considering a single clustering solution, where each cluster is described by its number of observations, cluster centre and some measure of the variability of observations around the cluster centre. It then shows how interactive data visualisation can be used to compare two clusterings, and investigating where the two solutions disagree.
Fields of Study & Subjects
Advanced Clustering Algorithms Research
Data Mining and Machine Learning Applications
Data Visualization and Analytics
Publication details
- Journal / source
- Chapman & Hall/CRC eBooks
- Pages
- 111–118
- Publication date
- 11 March 2026
- Language
- EN
- Document type
- Book chapter