This chapter describes a cluster analysis example using R software. We provide a quick start R code to compute and visualize K-means and hierarchical clustering.
Related BookPractical Guide to Cluster Analysis in R
Loading required R packages
clusterfor cluster analysis
factoextrafor cluster visualization
We’ll use the demo data set USArrests. We start by standardizing the data:
mydata <- scale(USArrests)
K-means is a clustering techniques that subdivide the data sets into a set of k groups, where k is the number of groups pre-specified by the analyst.
The following R codes show how to determine the optimal number of clusters and how to compute k-means and PAM clustering in R.
- Determining the optimal number of clusters: use
fviz_nbclust(mydata, kmeans, method = "gap_stat")
Suggested number of cluster: 3
- Compute and visualize k-means clustering:
set.seed(123) # for reproducibility km.res <- kmeans(mydata, 3, nstart = 25) # Visualize fviz_cluster(km.res, data = mydata, palette = "jco", ggtheme = theme_minimal())
Hierarchical clustering is an alternative approach to partitioning clustering for identifying groups in the data set. It does not require to pre-specify the number of clusters to be generated.
The result of hierarchical clustering is a tree-based representation of the objects, which is also known as dendrogram. Observations can be subdivided into groups by cutting the dendrogram at a desired similarity level.
- Computation: R function:
hclust(). It takes a dissimilarity matrix as an input, which is calculated using the function
R code to compute and visualize hierarchical clustering:
res.hc <- hclust(dist(mydata), method = "ward.D2") fviz_dend(res.hc, cex = 0.5, k = 4, palette = "jco")
A heatmap is another way to visualize hierarchical clustering. It’s also called a false colored image, where data values are transformed to color scale. Heat maps allow us to simultaneously visualize groups of samples and features. You can easily create a pretty heatmap using the R package
In heatmap, generally, columns are samples and rows are variables. Therefore we start by transposing the data before creating the heatmap.
library(pheatmap) pheatmap(t(mydata), cutree_cols = 4)
This chapter presents examples of R code to compute and visualize k-means and hierarchical clustering.
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For some reason my code produces two optimal clusters.
df = USArrests; df = scale(df)
# k-means clustering; ####
# optimal number of clusters;
method = “gap_stat”
I cant seem to find a difference in the code.
Same here! I suspect that the dataset got updated after the page was published.
I wish I will do the same heatmap but for agglomerative clustering. Is there a way to get this?