SSLR (version 0.9.3.3)

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

References

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

Examples

# NOT RUN {
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)


# }