fakmct | R Documentation |
Clustering data observation with hybrid method Fuzzy ART and K-Means
fakmct( input, rho, alpha, beta, w_init = NA, max_epochs = 1000, max_clusters = 1000, eps = 10^-6 )
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 |
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 |
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"))
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