clarans_clust: CLARANS clustering

In Ltochon/CLARA.seq: CLARA Clustering

Description

With the help of TraMineR package, CLARANS clustering provide a clustering of big dataset.
The main objective is to cluster state sequences with the "LCS" distance calculation method to find the best partition in N clusters.

WARNING : this function is less efficient than cLARA.

Usage

clarans_clust(
  data,
  nb_cluster,
  distargs = list(method = "LCS"),
  maxneighbours,
  numlocal,
  plot = FALSE,
  cores = detectCores() - 1
)

Arguments

data	The dataset to use. In case of sequences, use seqdef (from TraMineR package) to create such an object.
nb_cluster	The number of medoids
distargs	List with method parameters to apply. (See the function seqdist in TraMineR package)
maxneighbours	Number of neighbours to explore to find a better clustering
numlocal	Number of initialisation of the starting medoids
plot	Boolean variable to plot the research convergence
cores	Number of cores to use for parallelism

Value

An object of class clarans_seq

Examples

#creating sequences
library(TraMineR)
data(mvad)
mvad.labels <- c("employment", "further education", "higher education","joblessness", "school", "training")
mvad.scode <- c("EM", "FE", "HE", "JL", "SC", "TR")
mvad.seq <- seqdef(mvad, 17:86, states = mvad.scode,abels = mvad.labels, xtstep = 6)

#CLARANS Clustering
my_cluster <- clarans_clust(mvad.seq,nb_cluster = 4, maxneighbours = 20, numlocal = 4, plot = TRUE)

Ltochon/CLARA.seq documentation built on Dec. 17, 2021, 1:12 a.m.