This package is a implematation of k-means for longitudinal data (or trajectories).
Here is an overview of the package. For the description of the
algorithm, see kml.
Details
Package:
kml
Type:
Package
Version:
2.4.1
Date:
2016-02-02
License:
GPL (>= 2)
LazyData:
yes
Depends:
methods,clv,longitudinalData(>= 2.1.2)
URL:
http://www.r-project.org
URL:
http://christophe.genolini.free.fr/kml
Overview
To cluster data, KmL go through three steps, each of which
is associated to some functions:
Data preparation
Building "optimal" partition
Exporting results
1. Data preparation
KmL works on object of class ClusterLongData.
Data preparation therefore simply consists in transforming data into an object ClusterLongData.
This can be done via function
clusterLongData
(cld in short).
It converts a data.frame or a matrix into a ClusterLongData.
Instead of working on real data, one can also work on artificial
data. Such data can be created with
generateArtificialLongData
(gald in
short).
2. Building "optimal" partition
Once an object of class ClusterLongData has been created, the algorithm
kml can be run.
Starting with a ClusterLongData, kml built a Partition.
A object of class Partition is a partition of trajectories
into subgroups. It also contains some information like the
percentage of trajectories contained in each group or some quality critetion.
kml is a "hill-climbing" algorithm. The specificity of this
kind of algorithm is that it always converges towards a maximum, but
one cannot know whether it is a local or a global maximum. It offers
no guarantee of optimality.
To maximize one's chances of getting a quality Partition, it is better to run the hill climbing algorithm several times,
then to choose the best solution. By default, kml executes the hill climbing algorithm 20 times
and chooses the Partition maximizing the determinant of the matrix between.
Likewise, it is not possible to know beforehand the optimum number of clusters.
On the other hand, afterwards, it is possible to calculate
clues that will enable us to choose.
In the end, kml tests by default 2, 3, 4, 5 et 6 clusters, 20 times each.
3. Exporting results
When kml has constructed some
Partition, the user can examine them one by one and choose
to export some. This can be done via function
choice. choice opens a graphic windows showing
various information including the trajectories clutered by a specific
Partition.
When some Partition has been selected (the user can select
more than 1), it is possible to
save them. The clusters are therefore exported towards the file
name-cluster.csv. Criteria are exported towards
name-criteres.csv. The graphs are exported according to their
extension.
It is also possible to extract a partition from the object
ClusterLongData using the function getClusters.
### 1. Data Preparation
data(epipageShort)
names(epipageShort)
cldSDQ <- cld(epipageShort,timeInData=3:6,time=c(3,4,5,8))
### 2. Building "optimal" clusteration (with only 3 redrawings)
kml(cldSDQ,nbRedrawing=3,toPlot="both")
### 3. Exporting results
### To check the best's cluster numbers
plotAllCriterion(cldSDQ)
# To see the best partition
try(choice(cldSDQ))
### 4. Further analysis
epipageShort$clust <- getClusters(cldSDQ,4)
summary(glm(gender~clust,data=epipageShort,family="binomial"))