This tool implements the K-Means cluster analysis for grids in two variants, iterative minimum distance (Forgy 1965) and hill climbing (Rubin 1967).

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.

Name	Type	Identifier	Description	Constraints
Input
Features	grid list, input	GRIDS
Output
Clusters	grid, output	CLUSTER
Statistics	table, output	STATISTICS
Elbow Statistics	table, output	ELBOW_STATS
Options
Method	choice	METHOD		Available Choices: [0] Minimum Distance (Forgy 1965) [1] Hill Climbing (Rubin 1967) [2] Minimum Distance + Hill Climbing Default: 1
Number of Clusters	integer number	NCLUSTER		Minimum: 2 Default: 10
Maximum Iterations	integer number	MAXITER	Maximum number of iterations, ignored if zero.	Minimum: 0 Default: 10
Start Partition	choice	INITIALIZE		Available Choices: [0] random [1] periodical [2] keep values Default: 0
Start Partition	choice	ELBOW_INIT		Available Choices: [0] random [1] periodical Default: 0
Elbow Method	choice	ELBOW		Available Choices: [0] no [1] absolute [2] change [3] maximum distance Default: 0
Threshold Percentage	floating point number	ELBOW_ABS		Minimum: 0.000000 Maximum: 50.000000 Default: 10.000000
Threshold Percentage	floating point number	ELBOW_CHG		Minimum: 0.000000 Maximum: 50.000000 Default: 5.000000
Normalise	boolean	NORMALISE	Take standardized normalise grids by standard deviation before clustering.	Default: 0
Old Version	boolean	OLDVERSION	slower but memory saving	Default: 0