Point Clustering

Point Clustering: Various Approaches

Please fill this in with any approaches that you have tried for Point Clustering along with code snippets. Please include discussion on why a particular method worked well or didn't work well and what circumstances it may be good for.

Possible Approaches

• Coordinate interleaving (i.e. 1. rounding input coordinates, 2. grouping/aggregating them, and then 3. averaging their original coordinates so that the cluster position is at the weighted coordinate of all input geometries).
• K-means Clustering
• Hierarchical Clustering
• Distance calculation for each coordinate pair

Input Parameters

Depending on algorithm...

• Partitioning methods
  • Map grid width ("quare / manhattan world", see coordinate interleaving/rounding)
  • Some self-correlation threshold (see e.g. k-means)
  • Predefined irregular polygons (e.g. zip code boundaries)

Implementations

• MapServer CLUSTER

References

• Wikipedia Article on Data Clustering
• PostGIS Mailing List thread on clustering points
• Point Clustering Utility Trigger enhancement idea reported as ticket to PostGIS Trac.
• Here & here: Mapserver Mailing List threads on clustering points
• PyCluster: Python Cluster Functions
• Using Genetic Algorithms in Clustering Problems: paper from GeoComputation 2000 conference
• Automatic clustering via boundary extraction for mining massive point-data sets: paper from GeoComputation 2000 conference