calcBelongMatrix

Calculate the membership matrix according to a set of centroids, the observed
 data and the fuzziness degree

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

calcBelongMatrix function

<dl><dt>centers</dt>
<dd>A matrix or a dataframe representing the centers of the
clusters with p columns and k rows</dd>
<dt>data</dt>
<dd>A dataframe or matrix representing the observed data with n rows
and p columns</dd>
<dt>m</dt>
<dd>A float representing the fuzziness degree</dd>
<dt>sigmas</dt>
<dd>A numeric vector for calculating the robust version of the FCM. Filled with ones
if the classical version is required</dd></dl>

Arguments

Calculate the membership matrix — calcBelongMatrix

<dl>

<dt>centers</dt>
<dd>A matrix or a dataframe representing the centers of the
clusters with p columns and k rows</dd>


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


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


<dt>sigmas</dt>
<dd>A numeric vector for calculating the robust version of the FCM. Filled with ones
if the classical version is required</dd>

</dl>

calcBelongMatrix: Calculate the membership matrix

Description

Usage

Value

Arguments