calcSFGCMBelongMatrixNoisy

Calculate the generalized membership matrix (spatial version) with a noise cluster

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

calcSFGCMBelongMatrixNoisy function

<dl><dt>centers</dt>
<dd>A matrix representing the centers of the clusters with p
columns and k rows</dd>
<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 n rows
and p columns</dd>
<dt>m</dt>
<dd>A float 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>
<dt>beta</dt>
<dd>A float for the beta parameter (control speed convergence and classification crispness)</dd>
<dt>delta</dt>
<dd>A float, the value set for delta by the user</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>
<dt>wsigmas</dt>
<dd>Same as sigmas, but calculated on the spatially lagged dataset</dd></dl>

Arguments

Calculate the generalized membership matrix (spatial version) with a noise cluster — calcSFGCMBelongMatrixNoisy

<dl>

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


<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 n rows
and p columns</dd>


<dt>m</dt>
<dd>A float 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>


<dt>beta</dt>
<dd>A float for the beta parameter (control speed convergence and classification crispness)</dd>


<dt>delta</dt>
<dd>A float, the value set for delta by the user</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>


<dt>wsigmas</dt>
<dd>Same as sigmas, but calculated on the spatially lagged dataset</dd>

</dl>

calcSFGCMBelongMatrixNoisy: Calculate the generalized membership matrix (spatial version) with a noise cluster

Description

Usage

Value

Arguments