Tool Cluster Analysis for Point Clouds

Cluster analysis for point clouds.
This tool is a port of the 'Cluster Analysis for Grids' tool from the 'Imagery - Classification' tool library.

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: Volker Wichmann (c) 2010, LASERDATA GmbH
Menu: Shapes|Point Clouds|Classification

Parameters

	Name	Type	Identifier	Description	Constraints
Input	Point Cloud	Point Cloud, input	`PC_IN`	Input	-
Output	Result (*)	Point Cloud, output, optional	`PC_OUT`	Output	-
Output	Statistics	Table, output	`STATISTICS`	-	-
Options	Attributes	Table fields	`FIELDS`	The attribute fields to cluster	-
	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	`NCLUSTER`	Number of clusters	Minimum: 2 Default: 10
	Normalise	Boolean	`NORMALISE`	Automatically normalise attributes by standard deviation before clustering.	Default: 1
(*) optional

Command-line

Usage: saga_cmd pointcloud_tools 11 [-PC_IN <str>] [-FIELDS <str>] [-PC_OUT <str>] [-STATISTICS <str>] [-METHOD <str>] [-NCLUSTER <num>] [-NORMALISE <str>]
  -PC_IN:<str>     	Point Cloud
	Point Cloud, input
  -FIELDS:<str>    	Attributes
	Table fields
  -PC_OUT:<str>    	Result
	Point Cloud, output, optional
  -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
	Minimum: 2
	Default: 10
  -NORMALISE:<str> 	Normalise
	Boolean
	Default: 1