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.kml(Object, nbClusters = 2:6, nbRedrawing = 20, saveFreq = 100,
maxIt = 200, trajMinSize = 2, print.cal = FALSE,
print.traj = FALSE, imputationMethod = "copyMean",
distance, power = 2, centerMethod = meanNA, startingCond = "allMethods",
distanceStartingCond = "euclidean", ...)Clusterization kml must work. By default,
nbClusters is 2:6 which indicates that kml must
search partitions with respectively 2, theClusterizLongData
once in a wilde. saveFreq define the frequency of the saving
process. The ClusterizLongData istrajMinSize sets the
minimum number of values that a trajectory must contain not to be
excluded. For example, if the trajectories have imputationMethod define the method use to impute the
missing value. It should be one of
"LOCF","LOCB","linearInterpolation","linedistanceStartingCond define the distance that will be
use to calculate this matrix. It should be one of "euclidean",
"maximum",ClusterizLongData Clusterization to it.distanceis set to a classical
distance (one of "euclidean", "maximum", "manhattan", "canberra",
"binary" or "minkowski") andprint.trajis set toFALSE(the default),kmlcall a C
compiled (optimized) procedure.print.traj=TRUE,kmluses a R non compiled
programmes.Example section).
If for a specific use, you need a different distance, feel free to
contact the author.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 52 sublist called c1,
c2, c3 until c52. The sublist cX store the all Clusterization with
X clusters. Inside a sublist, the
Clusterization are sort from the biggest quality criterion to
the smallest (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 "clear" some
list, see Object["clusters","clear"]<-value in
ClusterizLongData).
The possible starting conditions are "randomAll", "randomK" and
"maxDist", as define in partitionInitialise. In
addition, the method "allMethods" is a shortcut that run a "maxDist", a "randomAll"
and "randomK" for all the other re rolling.http://christophe.genolini.free.fr/kmlkml-package
Classes : ClusterizLongData Clusterization clusterizLongData, choice### 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)Run the code above in your browser using DataLab