affectFuzzyIndiv

Given some longitudinal data (trajectories) and k cluster's centers, <code>affectFuzzyIndiv</code>
compute the matrix of individual membership (according to the algorithm
fuzzy k-means).

An implementation of k-means specifically design
to cluster longitudinal data. It provides facilities to deal with missing
value, compute several quality criterion (Calinski and Harabatz,
Ray and Turie, Davies and Bouldin, BIC, ...) and propose a graphical
interface for choosing the 'best' number of clusters.

Christophe Genolini

K-Means for Longitudinal Data

Bruno Falissard

Patrice Kiener

affectFuzzyIndiv function

<dl> <dt>traj</dt>
<dd><code>[matrix]</code>: longitudinal data. Each line is an individual,
 each column is a time measurement.</dd> <dt>clustersCenter</dt>
<dd><code>[matrix]</code>: cluster's centers. Each line is a
 cluster's center, each column is a time measurement.</dd>
 <dt>fuzzyfier</dt>
<dd><code>[numeric]</code>: value of the fuzzyfier used to
 compute individual's memberships.</dd>
</dl>

Arguments

~ Function: affectFuzzyIndiv ~ — affectFuzzyIndiv

<dl>

 <dt>traj</dt>
<dd><code>[matrix]</code>: longitudinal data. Each line is an individual,
 each column is a time measurement.</dd>

 <dt>clustersCenter</dt>
<dd><code>[matrix]</code>: cluster's centers. Each line is a
 cluster's center, each column is a time measurement.</dd>


 <dt>fuzzyfier</dt>
<dd><code>[numeric]</code>: value of the fuzzyfier used to
 compute individual's memberships.</dd>


</dl>

affectFuzzyIndiv: ~ Function: affectFuzzyIndiv ~

Description

Usage

Value

Arguments

Details

Examples