Dendrogram
Tree diagram showing hierarchical clustering - branches merge at similarity thresholds.
Food Category Clustering
Hierarchical grouping by similarity (merge distance)
View data (16 rows)
| Distance | Item | Parent |
|---|---|---|
| 0.2000 | Apple | Fruit |
| 0.2000 | Banana | Fruit |
| 0.2000 | Orange | Fruit |
| 0.3000 | Carrot | Vegetable |
| 0.3000 | Broccoli | Vegetable |
| 0.3000 | Spinach | Vegetable |
| 0.2500 | Chicken | Meat |
| 0.2500 | Beef | Meat |
| 0.2200 | Salmon | Fish |
| 0.2200 | Tuna | Fish |
| 0.5500 | Fruit | Produce |
| 0.5500 | Vegetable | Produce |
| 0.5000 | Meat | Protein |
| 0.5000 | Fish | Protein |
| 0.8500 | Produce | Food |
| 0.8500 | Protein | Food |
Use a dendrogram when…
- Hierarchical clustering results
- Taxonomies
- Organizational charts
Avoid when…
- Non-hierarchical relationships
- Very large trees (>100 leaves)
Data it needs
| Property | Value |
|---|---|
| Min Rows | 4 |
| Min Columns | 3 |
| Column Types | stringstringnumber |
Visual anatomy
Guiding principles
- DesignChart-Question Fit
Best for hierarchical structure questions
- IntegrityZero Baselines
Read merge distance (x-axis) as dissimilarity — joins farther left mean less similar (leaves sit on the right at distance 0)
- PerceptionGestalt Grouping
Choose the cut-line at a height where the resulting cluster count matches the question; cluster count is a function of where you slice
Consider instead
Common mistakes
Too many leaves making labels unreadable
Not showing merge distance
History
Standard output of hierarchical cluster analysis since Sokal and Sneath (1963).
Accessibility notes
Describe the hierarchical groupings as nested lists and announce merge distances and leaf counts per cluster — those numbers are the diagnostic, not the picture.
Related reading
Got data? Let's see what works.
Drop your CSV. You'll get a Dendrogram plus four alternatives - ranked by which one actually fits your data best.