belongsSGFCM

membership matrix calculator for SGFCM algorithm

internal

Provides functions to apply spatial fuzzy unsupervised classification, visualize and interpret results. This method is well suited when the user wants to analyze data with a fuzzy clustering algorithm and to account for the spatial dimension of the dataset. In addition, indexes for estimating the spatial consistency and classification quality are proposed.
The methods were originally proposed in the field of brain imagery (seed Cai and al. 2007 <doi:10.1016/j.patcog.2006.07.011> and Zaho and al. 2013 <doi:10.1016/j.dsp.2012.09.016>) and recently applied in geography (see Gelb and Apparicio <doi:10.4000/cybergeo.36414>).

Jeremy Gelb

geocmeans

Implementing Methods for Spatial Fuzzy Unsupervised
Classification

Philippe Apparicio

belongsSGFCM function

<dl><dt>data</dt>
<dd>a matrix (the dataset used for clustering)</dd>
<dt>centers</dt>
<dd>a matrix (the centers of the clusters)</dd>
<dt>dots</dt>
<dd>a list of other arguments specific to FCM</dd></dl>

Arguments

membership matrix calculator for SGFCM algorithm — belongsSGFCM

<dl>

<dt>data</dt>
<dd>a matrix (the dataset used for clustering)</dd>


<dt>centers</dt>
<dd>a matrix (the centers of the clusters)</dd>


<dt>dots</dt>
<dd>a list of other arguments specific to FCM</dd>

</dl>

belongsSGFCM: membership matrix calculator for SGFCM algorithm

Description

Usage

Value

Arguments