calcSWFCCentroids

Calculate the new centroids of the clusters based on the membership matrix for SFCM

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

calcSWFCCentroids function

<dl><dt>data</dt>
<dd>A matrix representing the observed data with n rows and p columns</dd>
<dt>wdata</dt>
<dd>A matrix representing the lagged observed data with nrows 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>An integer representing the fuzziness degree</dd>
<dt>alpha</dt>
<dd>A float representing the weight of the space in the analysis (0
is a typical fuzzy-c-mean algorithm, 1 is balanced between the two
dimensions, 2 is twice the weight for space)</dd></dl>

Arguments

Calculate the centroids of SFCM — calcSWFCCentroids

<dl>

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


<dt>wdata</dt>
<dd>A matrix representing the lagged observed data with nrows 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>An integer representing the fuzziness degree</dd>


<dt>alpha</dt>
<dd>A float representing the weight of the space in the analysis (0
is a typical fuzzy-c-mean algorithm, 1 is balanced between the two
dimensions, 2 is twice the weight for space)</dd>

</dl>

calcSWFCCentroids: Calculate the centroids of SFCM

Description

Usage

Value

Arguments