General LCVQE Algorithm — lcvqeSSLR • SSLR

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.

lcvqeSSLR(n_clusters = NULL, mustLink = NULL, cantLink = NULL, max_iter = 2)

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 2

Note

This models only returns labels, not centers

References

Dan Pelleg, Dorit Baras
K-means with large and noisy constraint sets
2007

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 <- lcvqeSSLR(max_iter = 1) %>% 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 3          
#>  2 3          
#>  3 3          
#>  4 3          
#>  5 3          
#>  6 3          
#>  7 3          
#>  8 3          
#>  9 3          
#> 10 3          
#> # ... with 140 more rows