Reference · Glossary
Clustering
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Exploratory analysis: segment users, theme open-ended survey answers, or bucket documents before labeling.
#When to use
Exploratory analysis: segment users, theme open-ended survey answers, or bucket documents before labeling.
#When not to
When you already have reliable labels and need a classifier. Clusters are hypotheses, not ground truth.
#Quality checklist
- Choose distance metric that matches your embedding space
- Inspect exemplar points per cluster
- Check stability when you re-run with new seeds
- Name clusters from evidence, not wishful thinking
#Example
Embed 2,000 support tickets → k-means (k=12)
Label each cluster by reading 10 random tickets
Promote stable themes into a taxonomy for classification#Learn next
- Lesson: `clustering-basics`
- Related: embedding