fakmct: Fuzzy Adaptive Resonance Theory (ART) K-Means Clustering...

In fakmct: Fuzzy Adaptive Resonance Theory K-Means Clustering Technique

Fuzzy Adaptive Resonance Theory (ART) K-Means Clustering Technique

Description

Clustering data observation with hybrid method Fuzzy ART and K-Means

Usage

fakmct(
  input,
  rho,
  alpha,
  beta,
  w_init = NA,
  max_epochs = 1000,
  max_clusters = 1000,
  eps = 10^-6
)

Arguments

input	The input (vector) data observation. Should be numeric type of data.
rho	Vigilance parameter in (0,1)
alpha	Choice parameter alpha > 0
beta	Learning rate in (0,1)
w_init	Initial weight
max_epochs	Maximum number of iterations
max_clusters	Maximum number of clusters that allowed
eps	Tolerance with default is 10^-6

Value

labels	clusters label of each observations
size	the size of each clusters that have been formed
clusters	a list of observations in each clusters
centroids	cluster centroids that are calculated by the mean value of the objects in each clusters
weights	the model weight
params	parameters that have been saved in the function
num_clusters	number of cluster that have been formed
running.time	time for running function

Examples

library(fakmct)
# Using dataset iris
## load data
data.inputs = iris[,-5]
true.labels = as.numeric(unlist(iris$Species))

## run model data
ex.iris<-fakmct(data.inputs, alpha = 0.3, rho = 0.5, beta = 1, max_epochs = 50, max_clusters = 5)
ex.iris$labels
ex.iris$size
ex.iris$centroids
ex.iris$params

## plot data
plot(data.inputs, col = ex.iris$labels, pch = true.labels,
     main = paste0("Dataset: Iris"))

# Using data IPM 2019

## load simulate data IPM
data("simulatedataIPM")
dt <- simulatedataIPM

## run model data IPM
mod.fakm<-fakmct(dt, alpha = 0.3, rho = 0.5, beta = 0.1, max_epochs = 50, max_clusters = 5)
mod.fakm$labels
mod.fakm$size
mod.fakm$centroids
mod.fakm$params

## plot data IPM
plot(dt, col = mod.fakm$labels, pch=mod.fakm$labels, main = paste0("Dataset IPM"))

fakmct documentation built on June 23, 2022, 1:06 a.m.