Nothing
knitr::opts_chunk$set( digits = 3, collapse = TRUE, comment = "#>" ) options(digits = 3)
We load de data:
library(tidyverse) library(caret) library(SSLR) library(tidymodels)
data(wine) data <- iris set.seed(1) #% LABELED cls <- which(colnames(iris) == "Species") labeled.index <- createDataPartition(data$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]))
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