cclsSSLR: General Interface Pairwise Constrained Clustering By Local...

In SSLR: Semi-Supervised Classification, Regression and Clustering Methods

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 documentation built on July 22, 2021, 9:08 a.m.