evaluateMatrices

Evaluate if the algorithm converged by comparing two successive membership
matrices. Calculate the absolute difference between the matrices and then
calculate the max of each row. If all the values of the final vector are
below the fixed tolerance, then return True, else return False

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

evaluateMatrices function

<dl><dt>mat1</dt>
<dd>A n X k matrix giving for each observation n, its probability to
belong to the cluster k at iteration i</dd>
<dt>mat2</dt>
<dd>A n X k matrix giving for each observation n, its probability to
belong to the cluster k at iteration i+1</dd>
<dt>tol</dt>
<dd>A float representing the algorithm tolerance</dd></dl>

Arguments

Matrix evaluation — evaluateMatrices

<dl>

<dt>mat1</dt>
<dd>A n X k matrix giving for each observation n, its probability to
belong to the cluster k at iteration i</dd>


<dt>mat2</dt>
<dd>A n X k matrix giving for each observation n, its probability to
belong to the cluster k at iteration i+1</dd>


<dt>tol</dt>
<dd>A float representing the algorithm tolerance</dd>

</dl>

evaluateMatrices: Matrix evaluation

Description

Usage

Value

Arguments

Examples