Fixed cluster counts create artificial boundaries. Dynamic resolution lets the data determine how detailed the map should be. When concepts are dense and evolving, the system zooms in. When concepts are stable, it stays wide.
The Self-Tuning Principle
Set a target average distance to centroid. If a cluster exceeds that distance, it splits. If a region is stable, it stays coarse. This creates a self-regulating structure that adapts as your dataset grows.
Benefits
- Avoids over-fragmentation in stable regions.
- Preserves nuance in fast-evolving regions.
- Eliminates the need for a single “correct” cluster count.
Multi-Resolution Exploration
You can explore at multiple levels: continents, cities, neighborhoods. This is crucial when the graph is too large to interpret at one scale. Clusters become waypoints, not endpoints.
Incremental Updates
Dynamic resolution supports incremental clustering. You do not need to rebuild the entire map. You update only regions affected by new data, keeping computation manageable and structure fresh.
Dynamic resolution is how a living system stays both coherent and flexible.