T4cluster is an R package designed as a computational toolkit with comprehensive coverage in relevant topics around the study of cluster analysis. It contains several classes of algorithms for
and other utility functions for further use. If you request additional functionalities or have suggestions, please contact maintainer.
You can install the released version of T4cluster from CRAN with:
And the development version from GitHub with:
# install.packages("devtools") devtools::install_github("kisungyou/T4cluster")
T4cluster offers a variety of clustering algorithms in common
interface. In this example, we show a basic pipeline with
dataset, which can be generated as follows;
# load the library library(T4cluster) # generate the data smiley = T4cluster::genSMILEY(n=200) data = smiley$data label = smiley$label # visualize plot(data, pch=19, col=label, xlab="", ylab="", main="SMILEY Data")
where each component of the face is considered as one cluster - the data
has 4 clusters. Here, we compare 4 different methods; (1) k-means
kmeans), (2) k-means++ (
kmeanspp), (3) gaussian mixture model
gmm), and (4) spectral clustering with normalized cuts (
# run algorithms run1 = T4cluster::kmeans(data, k=4) run2 = T4cluster::kmeanspp(data, k=4) run3 = T4cluster::gmm(data, k=4) run4 = T4cluster::scNJW(data, k=4, sigma = 0.1) # visualize par(mfrow=c(2,2)) plot(data, pch=19, xlab="", ylab="", col=run1$cluster, main="k-means") plot(data, pch=19, xlab="", ylab="", col=run2$cluster, main="k-means++") plot(data, pch=19, xlab="", ylab="", col=run3$cluster, main="gmm") plot(data, pch=19, xlab="", ylab="", col=run4$cluster, main="scNJW")
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