fakmct {fakmct}

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"))