Hierarchical Clustering
A bottom-up method of clustering is hierarchical clustering. By gradually merging or separating clusters, it builds a hierarchy of clusters. A dendrogram, a structure like a tree, is frequently used to represent the outcome. The dendrogram can be clipped at a given height to produce the required number of clusters.
Dendrogram
A cluster graph, also known as a dendrogram or a tree diagram, is a graphical representation of the clustering results. It shows how data points or clusters are grouped together and can provide insights into the hierarchical structure of your data. Two popular methods for creating cluster graphs are hierarchical clustering and k-means clustering.
Hierarchical Clustering in R
R
# Generate random dataset.seed(123)data <- matrix(rnorm(50), ncol = 2)# Perform hierarchical clusteringhi_cl <- hclust(dist(data))# Plot the dendrogramplot(hi_cl) |
Output:
In this example, we first generate random data and then perform hierarchical clustering using the hclust function. We use the Euclidean distance (dist) as the dissimilarity measure. and generates a dendrogram visualization of the hierarchical clustering results. You can cut the dendrogram at a specific height to obtain clusters.
Cluster Graph in R
R’s cluster graph functionality can be a useful tool for visualizing data and seeing patterns within it. In disciplines including biology, the social sciences, and data analysis, cluster graphs are frequently used to group together related data points. In this article, we’ll demonstrate how to display a cluster graph in R by combining the ggplot2 package for data analysis and visualization with the ggraph tool for graph visualization.
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