kml2.4.6.1 package

K-Means for Longitudinal Data

affectFuzzyIndiv

~ Function: affectFuzzyIndiv ~

affectIndiv

~ Functions: affectIndiv & affectIndivC ~

calculTrajFuzzyMean

~ Function: calculTrajFuzzyMean ~

calculTrajMean

~ Functions: calculTrajMean & calculTrajMeanC ~

choice

~ Function: choice ~

ClusterLongData-class

~ Class: ClusterLongData ~

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-internal

~ Internal KmL objects and methods ~

kml-package

~ Overview: K-means for Longitudinal data ~

kml

~ Algorithm kml: K-means for Longitidinal data ~

ParKml-class

~ Class: "ParKml" ~

parKml

~ Function: parKml ~

plot

~ Function: plot for ClusterLongData ~

plotMeans

~ Function: plotMeans for ClusterLongData ~

plotTraj

~ Function: plotTraj for ClusterLongData ~

Download source packageRead PDF manual

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.