K-Means Clustering for Grids
- Author: O.Conrad (c) 2001
- Menu: Imagery | Classification | Unsupervised
Description
This tool implements the K-Means cluster analysis for grids in two variants, iterative minimum distance (Forgy 1965) and hill climbing (Rubin 1967).
References
- Forgy, E. (1965): Cluster analysis of multivariate data: efficiency vs. interpretability of classifications. Biometrics 21:768.
- Rubin, J. (1967): Optimal classification into groups: an approach for solving the taxonomy problem. J. Theoretical Biology, 15:103-144.
Parameters
| | Name | Type | Identifier | Description | Constraints |
|---|
| Input | Grids | grid list, input | GRIDS | - | - |
| Output | Clusters | grid, output | CLUSTER | - | - |
| Statistics | table, output | STATISTICS | - | - |
| Options | Grid System | grid system | PARAMETERS_GRID_SYSTEM | - | - |
| Method | choice | METHOD | - | Available Choices:
[0] Iterative Minimum Distance (Forgy 1965)
[1] Hill-Climbing (Rubin 1967)
[2] Combined Minimum Distance / Hillclimbing
Default: 1 |
| Clusters | integer number | NCLUSTER | Number of clusters | Minimum: 2
Default: 10 |
| Maximum Iterations | integer number | MAXITER | Maximum number of iterations, ignored if set to zero. | Minimum: 0
Default: 10 |
| Normalise | boolean | NORMALISE | Automatically normalise grids by standard deviation before clustering. | Default: 0 |
| Update Colors from Features | boolean, GUI | RGB_COLORS | Use the first three features in list to obtain blue, green, red components for class colour in look-up table. | Default: 0 |
| Start Partition | choice | INITIALIZE | - | Available Choices:
[0] random
[1] periodical
[2] keep values
Default: 0 |
| Old Version | boolean | OLDVERSION | slower but memory saving | Default: 0 |
| Update View | boolean, GUI | UPDATEVIEW | - | Default: 1 |
Command Line
Usage: saga_cmd imagery_classification 1 [-GRIDS ] [-CLUSTER ] [-STATISTICS ] [-METHOD ] [-NCLUSTER ] [-MAXITER ] [-NORMALISE ] [-INITIALIZE ] [-OLDVERSION ]
-GRIDS: Grids
grid list, input
-CLUSTER: Clusters
grid, output
-STATISTICS: Statistics
table, output
-METHOD: Method
choice
Available Choices:
[0] Iterative Minimum Distance (Forgy 1965)
[1] Hill-Climbing (Rubin 1967)
[2] Combined Minimum Distance / Hillclimbing
Default: 1
-NCLUSTER: Clusters
integer number
Minimum: 2
Default: 10
-MAXITER: Maximum Iterations
integer number
Minimum: 0
Default: 10
-NORMALISE: Normalise
boolean
Default: 0
-INITIALIZE: Start Partition
choice
Available Choices:
[0] random
[1] periodical
[2] keep values
Default: 0
-OLDVERSION: Old Version
boolean
Default: 0