General Interface Seeded KMeans — seeded

The difference with traditional Kmeans is that in this method implemented, at initialization, there are as many clusters as the number of classes that exist of the labelled data, the average of the labelled data of a given class

seeded_kmeans(max_iter = 10, method = "euclidean")

Arguments

max_iter	maximum iterations in KMeans. Default is 10
method	distance method in KMeans: "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski"

References

Sugato Basu, Arindam Banerjee, Raymond Mooney
Semi-supervised clustering by seeding
July 2002 In Proceedings of 19th International Conference on Machine Learning

Examples

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

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



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

#Get labels (assing clusters), type = "raw" return factor
labels <- m %>% cluster_labels()

print(labels)
#> # A tibble: 150 x 1
#>    .pred_class
#>    <fct>      
#>  1 1          
#>  2 1          
#>  3 1          
#>  4 1          
#>  5 1          
#>  6 1          
#>  7 1          
#>  8 1          
#>  9 1          
#> 10 1          
#> # ... with 140 more rows


#Get centers
centers <- m %>% get_centers()

print(centers)
#>          [,1]     [,2]     [,3]     [,4]
#> [1,] 5.006000 3.428000 1.462000 0.246000
#> [2,] 5.883607 2.740984 4.388525 1.434426
#> [3,] 6.853846 3.076923 5.715385 2.053846