What it is
Hierarchical clustering progressively merges or splits observations to reveal a nested grouping structure often visualized with a dendrogram.
Overview notes
Useful role
This method is often best used to understand the structure of the problem before a final operational clustering step.
Decision guide
When to use it
- When you want to explore clustering structure before fixing a final solution
- When a dendrogram helps explain grouping logic
- When cluster count is still uncertain
When not to use it
- When the sample is very large and speed matters
- When you need a quick production-ready assignment method
Inputs required
- Standardized variables or a valid distance matrix
- A linkage choice
Typical outputs
- Dendrogram
- Candidate cluster solutions
Simple example
Examine how respondents cluster on attitude statements to decide whether a three four or five cluster solution is more defensible.
Strengths
- Exploratory and visually intuitive
- Helpful for judging cluster count
Limitations
- Can be computationally heavier
- Sensitive to distance and linkage choices
Common mistakes
- Treating one dendrogram cut as obviously correct
- Ignoring the effect of preprocessing and linkage selection
How I use it in practice
I use hierarchical clustering when the team still needs to understand the structure of the data before locking a final segmentation solution.
What is outputted
- Dendrogram and candidate cluster cuts
How to interpret the output
- Look for stable interpretable branches rather than forced symmetry
How to communicate to clients
- Explain what the dendrogram shows and what it does not prove
Displayr / Q implementation notes
- Preserve the distance and linkage settings in the project notes
Visual placeholder
Dendrogram placeholder
Add a screenshot of a dendrogram and annotate where a plausible cut might happen.
Recommended placeholder: chart screenshot, process diagram, output interpretation notes, and one short caption on what to inspect first.