Difference between revisions of "Point Clustering"
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=== Possible Approaches: === | === Possible Approaches: === | ||
| − | * Coordinate interleaving | + | * 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 |
| − | * | + | * Distance calculation for each coordinate pair |
| + | |||
| + | === Implementations === | ||
| + | * [http://mapserver.org/mapfile/cluster.html MapServer CLUSTER] | ||
=== References === | === References === | ||
Revision as of 11:51, 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
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