fuzzy_clust: FUZZY-CLARA clustering

In Ltochon/CLARA.seq: CLARA Clustering

Description

With the help of TraMineR package, FUZZY-CLARA 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.
This function is a mix between CLARA and the ROBUST FUZZY C-MEDOIDS.

WARNING : This function is not finished yet !

Usage

fuzzy_clust(
  data,
  nb_sample = 100,
  size_sample = 40 + 2 * nb_cluster,
  nb_cluster = 4,
  distargs = list(method = "LCS"),
  fuzzyfier = 2,
  p = 5,
  threshold = 10,
  max_iter = 10,
  noise = 0.5,
  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_sample	The number of subsets to test.
size_sample	The size of each subset
nb_cluster	The number of medoids
distargs	List with method parameters to apply. (See the function seqdist in TraMineR package)
fuzzyfier	Value of the fuzzifier (default is 2, which is the traditionnal value)
p	Number of candidate to test to be a better medoid
threshold	Variable to exclude outliers, whose values are greater than threshold
max_iter	Number of maximal iteration to do to find the set of medoids
noise	Small value to avoid divisions by 0 error
plot	Boolean variable to plot the research convergence
cores	Number of cores to use for parallelism

Value

An object of class clarafuzzy_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)

#CLARA-FUzZY Clustering
my_cluster <- fuzzy_clust(mvad.seq,nb_sample = 14, size_sample = 50, plot = TRUE, threshold = 7, max_iter = 10, p=5)

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