Cluster Analysis for Point Clouds

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

Description

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.

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 number	`NCLUSTER`	Number of clusters	Minimum: 2 Default: 10
	Normalise	boolean	`NORMALISE`	Automatically normalise attributes by standard deviation before clustering.	Default: 1

Command Line


Usage: saga_cmd pointcloud_tools 11 [-PC_IN ] [-FIELDS ] [-PC_OUT ] [-STATISTICS ] [-METHOD ] [-NCLUSTER ] [-NORMALISE ]
  -PC_IN:     	Point Cloud
	point cloud, input
  -FIELDS:    	Attributes
	table fields
  -PC_OUT:    	Result
	point cloud, output, optional
  -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
  -NORMALISE: 	Normalise
	boolean
	Default: 1