General Interface MPC K-Means Algorithm

Model from conclust
This function takes an unlabeled dataset and two lists of must-link and cannot-link constraints as input and produce a clustering as output.

mpckmSSLR(n_clusters = NULL, mustLink = NULL, cantLink = NULL, max_iter = 10)

Arguments

n_clusters	A number of clusters to be considered. Default is NULL (num classes)
mustLink	A list of must-link constraints. NULL Default, constrints same label
cantLink	A list of cannot-link constraints. NULL Default, constrints with different label
max_iter	maximum iterations in KMeans. Default is 10

Note

This models only returns labels, not centers

References

Bilenko, Basu, Mooney
Integrating Constraints and Metric Learning in Semi-Supervised Clustering
2004

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 <- mpckmSSLR() %>% 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