What it is
K-means is a clustering algorithm that allocates each case to one of K clusters based on distance to a cluster centroid.
Overview notes
Practical note
K-means is often most useful when paired with other exploratory steps instead of treated as a one-click truth machine.
Decision guide
When to use it
- When you already have a likely range for the number of clusters
- When variables are numeric and standardized
- When you need a relatively efficient baseline segmentation algorithm
When not to use it
- When the data has strong outliers or non-spherical structure
- When the number of clusters is completely unknown
- When interpretability requires a more exploratory clustering path
Inputs required
- Numeric standardized variables
- A tested value of K
- Multiple random starts or stability checks
Typical outputs
- Cluster assignments
- Cluster centroids
- Separation diagnostics
Simple example
Run a four-cluster solution on standardized need-state batteries to create a first segmentation candidate.
Strengths
- Fast and widely understood
- Useful as a practical baseline
Limitations
- Requires K in advance
- Sensitive to initialization and scaling
Common mistakes
- Using raw variables with different scales
- Choosing K only because it looks neat in a presentation
How I use it in practice
I use K-means as a candidate generator rather than an automatic answer. It becomes useful once combined with stability checks and strong segment profiling.
What is outputted
- Cluster IDs and centroids
How to interpret the output
- Compare centroids and sizes before naming clusters
How to communicate to clients
- Position it as one way to derive a segmentation solution not the whole story
Displayr / Q implementation notes
- Keep preprocessing steps documented so the solution can be reproduced
Visual placeholder
K-means output placeholder
Add a centroid table or cluster-profile heatmap screenshot here later.
Recommended placeholder: chart screenshot, process diagram, output interpretation notes, and one short caption on what to inspect first.