Difference between revisions of "Point Clustering"
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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. | 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 | + | === 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). | * 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 | * K-means Clustering | ||
* Hierarchical Clustering | * Hierarchical Clustering | ||
* Distance calculation for each coordinate pair | * 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 === | === Implementations === | ||
Revision as of 10:57, 12 October 2014
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
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