Description Usage Arguments Note References Examples
View source: R/pairwise_constraints_clustering.R
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.
1 | ckmeansSSLR(n_clusters = NULL, mustLink = NULL, cantLink = NULL, max_iter = 10)
|
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 |
This models only returns labels, not centers
Wagstaff, Cardie, Rogers, Schrodl
Constrained K-means Clustering with Background Knowledge
2001
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | 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 <- ckmeansSSLR() %>% fit(Species ~ ., data)
#Get labels (assing clusters), type = "raw" return factor
labels <- m %>% cluster_labels()
print(labels)
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