calcRobustSigmas

Calculate sigmas for the robust version of the c-means 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

calcRobustSigmas function

<dl><dt>data</dt>
<dd>A Numeric matrix representing the observed data with n rows
and p columns</dd>
<dt>belongmatrix</dt>
<dd>A n X k matrix giving for each observation n, its
probability to belong to the cluster k</dd>
<dt>centers</dt>
<dd>A c X k matrix giving for each cluster c, its center in k dimensions</dd>
<dt>m</dt>
<dd>A float representing the fuzziness degree</dd></dl>

Arguments

Calculate sigmas for the robust version of the c-means algorithm — calcRobustSigmas

<dl>

<dt>data</dt>
<dd>A Numeric matrix representing the observed data with n rows
and p columns</dd>


<dt>belongmatrix</dt>
<dd>A n X k matrix giving for each observation n, its
probability to belong to the cluster k</dd>


<dt>centers</dt>
<dd>A c X k matrix giving for each cluster c, its center in k dimensions</dd>


<dt>m</dt>
<dd>A float representing the fuzziness degree</dd>

</dl>

calcRobustSigmas: Calculate sigmas for the robust version of the c-means algorithm

Description

Value

Arguments