calcEuclideanDistance2

euclidean distance between rows of a matrix and a vector

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

calcEuclideanDistance2 function

<dl><dt>y</dt>
<dd>a matrix</dd>
<dt>x</dt>
<dd>a vector (same length as ncol(matrix))</dd></dl>

Arguments

euclidean distance between rows of a matrix and a vector — calcEuclideanDistance2

<dl>

<dt>y</dt>
<dd>a matrix</dd>


<dt>x</dt>
<dd>a vector (same length as ncol(matrix))</dd>

</dl>

calcEuclideanDistance2: euclidean distance between rows of a matrix and a vector

Description

Usage

Value

Arguments