Tool K-Means Clustering for Grids

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.

Author: O.Conrad (c) 2001
Menu: Imagery|Classification|Unsupervised

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Grids	grid list, input	`GRIDS`	-	-
Output	Clusters	grid, output	`CLUSTER`	-	-
Output	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 (default)	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 <str>] [-CLUSTER <str>] [-STATISTICS <str>] [-METHOD <str>] [-NCLUSTER <num>] [-MAXITER <num>] [-NORMALISE <str>] [-INITIALIZE <str>] [-OLDVERSION <str>]
  -GRIDS:<str>     	Grids
	grid list, input
  -CLUSTER:<str>   	Clusters
	grid, output
  -STATISTICS:<str>	Statistics
	table, output
  -METHOD:<str>    	Method
	choice
	Available Choices:
	[0] Iterative Minimum Distance (Forgy 1965)
	[1] Hill-Climbing (Rubin 1967)
	[2] Combined Minimum Distance / Hillclimbing
	Default: 1
  -NCLUSTER:<num>  	Clusters
	integer number
	Minimum: 2
	Default: 10
  -MAXITER:<num>   	Maximum Iterations
	integer number
	Minimum: 0
	Default: 10
  -NORMALISE:<str> 	Normalise
	boolean
	Default: 0
  -INITIALIZE:<str>	Start Partition
	choice
	Available Choices:
	[0] random
	[1] periodical
	[2] keep values
	Default: 0
  -OLDVERSION:<str>	Old Version
	boolean
	Default: 0