Tool NameISODATA Clustering for Grids
Tool ID0
Library IDimagery_isocluster
Version1.0
Author(s)O.Conrad (c) 2016

Description

This tool executes the Isodata unsupervised classification - clustering algorithm. Isodata stands for Iterative Self-Organizing Data Analysis Techniques. This is a more sophisticated algorithm which allows the number of clusters to be automatically adjusted during the iteration by merging similar clusters and splitting clusters with large standard deviations. The tool is based on Christos Iosifidis' Isodata implementation.

References

Memarsadeghi, N., Mount, D. M., Netanyahu, N. S., Le Moigne, J. (2007): A Fast Implementation of the ISODATA Clustering Algorithm. International Journal of Computational Geometry and Applications, 17, 71-103. online

isodata.c (Christos Iosifidis)

A Fast Implementation of the ISODATA Clustering Algorithm

Parameters

NameTypeIdentifierDescriptionConstraints
Input
Featuresgrid list, inputFEATURES
Output
Clustersgrid, outputCLUSTER
Statisticstable, outputSTATISTICS
Options
NormalizebooleanNORMALIZEDefault: 0
Maximum Number of Iterationsinteger numberITERATIONSMinimum: 3
Default: 20
Initial Number of Clustersinteger numberCLUSTER_INIMinimum: 0
Default: 5
Maximum Number of Clustersinteger numberCLUSTER_MAXMinimum: 3
Default: 16
Minimum Number of Samples in a Clusterinteger numberSAMPLES_MINMinimum: 2
Default: 5
Start PartitionchoiceINITIALIZEAvailable Choices:
[0] random
[1] periodical
[2] keep values
Default: 0