SSLR: seeded_kmeans – R documentation

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seeded_kmeans

General Interface Seeded KMeans

Description

The difference with traditional Kmeans is that in this method implemented, at initialization, there are as many clusters as the number of classes that exist of the labelled data, the average of the labelled data of a given class

Usage

seeded_kmeans(max_iter = 10, method = "euclidean")

Arguments

`max_iter`	maximum iterations in KMeans. Default is 10
`method`	distance method in KMeans: "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski"

References

Sugato Basu, Arindam Banerjee, Raymond Mooney
Semi-supervised clustering by seeding
July 2002 In Proceedings of 19th International Conference on Machine Learning

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 <- seeded_kmeans() %>% fit(Species ~ ., data)

#Get labels (assing clusters), type = "raw" return factor
labels <- m %>% cluster_labels()

print(labels)


#Get centers
centers <- m %>% get_centers()

print(centers)

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