SSLR: cclsSSLR – R documentation

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cclsSSLR

General Interface Pairwise Constrained Clustering By Local Search

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

cclsSSLR(
  n_clusters = NULL,
  mustLink = NULL,
  cantLink = NULL,
  max_iter = 1,
  tabuIter = 100,
  tabuLength = 20
)

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 1
`tabuIter`	Number of iteration in Tabu search
`tabuLength`	The number of elements in the Tabu list

Note

This models only returns labels, not centers

References

Tran Khanh Hiep, Nguyen Minh Duc, Bui Quoc Trung
Pairwise Constrained Clustering by Local Search
2016

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 <- cclsSSLR(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