boot_worker

Worker function for cluster bootstrapping

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

boot_worker function

<dl><dt>object</dt>
<dd>A FCMres object, typically obtained from functions CMeans, GCMeans, SFCMeans, SGFCMeans</dd>
<dt>wdata</dt>
<dd>The lagged dataset if necessary, can be NULL if not required</dd>
<dt>tol</dt>
<dd>The tolerance criterion used in the evaluateMatrices function for
convergence assessment</dd>
<dt>maxiter</dt>
<dd>An integer for the maximum number of iteration</dd>
<dt>init</dt>
<dd>A string indicating how the initial centres must be selected. "random"
indicates that random observations are used as centres. "kpp" use a distance based method
resulting in more dispersed centres at the beginning. Both of them are heuristic.</dd></dl>

Arguments

Worker function for cluster bootstrapping — boot_worker

<dl>

<dt>object</dt>
<dd>A FCMres object, typically obtained from functions CMeans, GCMeans, SFCMeans, SGFCMeans</dd>


<dt>wdata</dt>
<dd>The lagged dataset if necessary, can be NULL if not required</dd>


<dt>tol</dt>
<dd>The tolerance criterion used in the evaluateMatrices function for
convergence assessment</dd>


<dt>maxiter</dt>
<dd>An integer for the maximum number of iteration</dd>


<dt>init</dt>
<dd>A string indicating how the initial centres must be selected. "random"
indicates that random observations are used as centres. "kpp" use a distance based method
resulting in more dispersed centres at the beginning. Both of them are heuristic.</dd>

</dl>

boot_worker: Worker function for cluster bootstrapping

Description

Usage

Value

Arguments

Details

Examples