Top-down hierarchical clustering method that recursively splits data into increasingly homogeneous subgroups.
How It Works
- Start with all data points in a single cluster.
- Recursively split the cluster using a flat clustering algorithm (e.g. k-means, bisecting k-means).
- Evaluate splits based on dissimilarity metrics (e.g. variance, distance).
- Repeat until stopping criteria are met (e.g. minimum dispersion, max depth, or target number of clusters).
What to Look For
- Global structure awareness: Splits are informed by overall data distribution.
- More scalable than agglomerative methods for large datasets.
- Useful for semantic taxonomies and progressive refinement.
- Ideal for documentation scaffolding where top-down modularity is key.
Application Models
- Hierarchical Clustering (top-down variant)