calcCentroids

Calculate the new centroids of the clusters based on the membership matrix
for a classical FCM.

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

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

Arguments

Calculate the centroids — calcCentroids

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

</dl>

calcCentroids: Calculate the centroids

Description

Value

Arguments