Quantitative/ Analysis
Cluster analysis
Discover a defensible grouping of observations
Cluster analysis groups observations according to a chosen similarity or probabilistic model. K-means uses distances to centroids, hierarchical methods build nested partitions, and mixture models estimate probabilistic membership. The number and meaning of clusters depend on variables, scaling and assumptions. A discovered grouping requires stability and external validation before it becomes a typology.
WHEN IT FITS
Use for exploratory segmentation or pattern discovery when group labels are unknown. Select substantively relevant features and a distance or distribution model suited to their scales and expected structure.
Strengths
- Reveals multivariate structure without predefined labels
- Probabilistic methods express membership uncertainty
Limitations
- Algorithms can impose groups on continuous variation
- Solutions depend strongly on scaling and tuning choices
Know the boundary
Clusters do not establish natural kinds, diagnoses or causally distinct populations.
USED ACROSS
Banking & financeBusiness & MBAPsychologyComputer scienceEducation