SSLR: lcvqeSSLR – R documentation

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lcvqeSSLR

General LCVQE Algorithm

Description

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.

Usage

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)

SSLR

Semi-Supervised Classification, Regression and Clustering Methods

v0.9.3.1

GPL-3

Authors

Francisco Jesús Palomares Alabarce [aut, cre] (<https://orcid.org/0000-0002-0499-7034>), José Manuel Benítez [ctb] (<https://orcid.org/0000-0002-2346-0793>), Isaac Triguero [ctb] (<https://orcid.org/0000-0002-0150-0651>), Christoph Bergmeir [ctb] (<https://orcid.org/0000-0002-3665-9021>), Mabel González [ctb] (<https://orcid.org/0000-0003-0152-444X>)

Initial release