kppCenters

Select the initial centers of centroids by using the k++ approach
as suggested in this article: http://ilpubs.stanford.edu:8090/778/1/2006-13.pdf

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

kppCenters function

<dl><dt>data</dt>
<dd>The dataset used in the classification</dd>
<dt>k</dt>
<dd>The number of groups for the classification</dd></dl>

Arguments

kpp centers selection — kppCenters

<dl>

<dt>data</dt>
<dd>The dataset used in the classification</dd>


<dt>k</dt>
<dd>The number of groups for the classification</dd>

</dl>

kppCenters: kpp centers selection

Description

Usage

Value

Arguments

Examples