clustering"

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

knitr::opts_chunk$set(
  digits = 3,
  collapse = TRUE,
  comment = "#>"
)
options(digits = 3)

R Markdown

We load de data:

library(tidyverse)
library(caret)
library(SSLR)
library(tidymodels)

data(wine)

data <- iris

set.seed(1)
#% LABELED
cls <- which(colnames(iris) == "Species")

labeled.index <- createDataPartition(data$Species, p = .2, list = FALSE)
data[-labeled.index,cls] <- NA

For example, we can train with Constrained Kmeans:

m <- constrained_kmeans() %>% fit(Species ~ ., data)

Labels:

m %>% cluster_labels()

Centers:

m %>% get_centers()

We can plot clusters with factoextra:

library(factoextra)
fviz_cluster(m$model, as.matrix(data[,-cls]))

SSLR documentation built on July 22, 2021, 9:08 a.m.