kml (version 0.9.2)

kml: ~ Algorithm kml: K-means for Longitidinal data ~

Description

KmL is a new implematation of k-means for longitudinal data (or trajectories). This algorithm is able to deal with missing value and provides an easy way to re roll the algorithm several times, varying the starting conditions and/or the number of clusters looked for. Here is the description of the algorithm. For an overview of the package, see kml-package.

Usage

kml(Object, nbClusters = 2:6, nbRedrawing = 20, saveFreq = 100,
    maxIt = 200, printCal = FALSE, printTraj = FALSE,
    distance=function(x,y){return(dist(t(cbind(x,y))))})

Arguments

Object

[ClusterizLongData]: contains trajectories to clusterize as well as pre-existing clusterizations.

nbClusters

[vector(numeric)]: Vector containing the number of clusters with which kml must work. By default, nbClusters is 2:6 which indicates that kml must search partitions with respectively 2, the

nbRedrawing

[numeric]: Sets the number of k-means (with different starting conditions) that must be run for each number of clusters.

saveFreq

[numeric]: Long computations can take several days. So it is possible to save the object ClusterizLongData once in a wilde. saveFreq define the frequency of the saving process. The ClusterizLongData is

maxIt

[numeric]: Sets a limit to the number of iteration if convergeance is not reach.

printCal

[logical]: If TRUE, the calinski criterion will be print on screen during computation (if the number of redrawing is big, this can slow the overall calculation process).

printTraj

[logical]: If TRUE, each step of k-means is print on screen during the calculation. This slow the overall calculation process by a factor 25, see "optimisation" below.

distance

[function(numeric,numeric)] function that compute the distance between two trajectories. The default function is the Euclidian distance with Gower adjustment (Gower adjustment takes in accomp missing value.) Changing the distance can slow t

Value

• A ClusterizLongData object, after having added some Clusterization to it.

Optimisation

Behind kml, even if the final user does not see it, there is two different procedure :

1. Fast: when the user does not change the defaultdistance(Euclidean with Gower adjustement) and the defaultprintTraj(FALSE), kml call a C compiled and optimized procedure.
2. Slow: when the user define its own distance, or if he wants to see the construction of the clusters, kml uses a R non compiled programmes.

The C prodecure is 25 times faster than the R one. So we advice to use the R procedure 1/ for trying some new method (like using a new distance) or 2/ to "see" the very first cluster construction, in order to check that every thing goes right, then to sweetch to the C procedure (like we do in Example section). If for a specific use, you need a different distance, feel free to contact the author.

Author(s)

Christophe Genolini PSIGIAM: Paris Sud Innovation Group in Adolescent Mental Health INSERM U669 / Maison de Solenn / Paris Contact author:

English translation

Rapha�l Ricaud Laboratoire "Sport & Culture" / "Sports & Culture" Laboratory University of Paris 10 / Nanterre

Details

kml works on object of class ClusterizLongData. For each number included in nbClusters, kml compute a Clusterization then stores it in the field clusters of the object ClusterizLongData according to its number of clusters. The algorithm starts over as many times as it is told in nbRedrawing. By default, it is executed for 2, 3, 4, 5 and 6 clusters 20 times each, namely 100 times. When a Clusterization has been found, it is added to the slot clusters. clusters is a list of 25 sublist called c1, c2, c3 until c25. The sublist cX store the all Clusterization with X clusters. Inside a sublist, the Clusterization are sort from the biggest Calinski criterion to the smallest. So the best are stored first. Note that Clusterization are saved throughout the algorithm. If the user interrupt the execution of kml, the result is not lost. If the user run kml on an object, then run kml again on the same object, the Clusterization that are computed the second time are added to the one allready present in the object (unless you use "clear" some list, see "Object["clusters","clear"]<-value" in ClusterizLongData).

References

Article submited Web site: http://christophe.genolini.free.fr/kml

See Also

Overview: kml-package Classes : ClusterizLongData, Clusterization, ArtificialLongData Methods : clusterizLongData, generateArtificialLongData, choice

Examples

### Generation of some data
cld1 <- as.cld(generateArtificialLongData())

### We suspect 2, 3, 4 or 5 clusters, we want 3 redrawing.
#     We want to "see" what happen (so printCal and printTraj are TRUE)
kml(cld1,2:6,3,printCal=TRUE,printTraj=TRUE)

### 4 seems to be the best. But to be sure, we try more redrawing 4 or 6 only.
#     We don't want to see again, we want to get the result as fast as possible.
kml(cld1,c(4,6),10)