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kml

K-Means for Longitudinal Data

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.

Functions (20)

ClusterLongData-class

~ Class: ClusterLongData ~

ParKml-class

~ Class: "ParKml" ~

affectFuzzyIndiv

~ Function: affectFuzzyIndiv ~

affectIndiv

~ Functions: affectIndiv & affectIndivC ~

calculTrajFuzzyMean

~ Function: calculTrajFuzzyMean ~

calculTrajMean

~ Functions: calculTrajMean & calculTrajMeanC ~

choice

~ Function: choice ~

clusterLongData

~ Function: clusterLongData (or cld) ~

epipageShort

~ Data: epipageShort ~

fuzzyKmlSlow

~ Algorithm fuzzy kml: Fuzzy k-means for Longitidinal data ~

generateArtificialLongData

~ Function: generateArtificialLongData (or gald) ~

getBestPostProba

~ Function: getBestPostProba ~

getCluster

~ Function: getClusters ~

kml

~ Algorithm kml: K-means for Longitidinal data ~

kml-internal

~ Internal KmL objects and methods ~

kml-package

~ Overview: K-means for Longitudinal data ~

parKml

~ Function: parKml ~

plot

~ Function: plot for ClusterLongData ~

plotMeans

~ Function: plotMeans for ClusterLongData ~

plotTraj

~ Function: plotTraj for ClusterLongData ~

kml

K-Means for Longitudinal Data

v2.4.1
GPL (>= 2)
Authors
Christophe Genolini [cre, aut], Bruno Falissard [ctb]
Initial release
2016-02-02

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