Performing Hierarchical clustering on Dataset
Using Hierarchical Clustering algorithm on the dataset using hclust() which is pre-installed in stats package when R is installed.
R
# Finding distance matrixdistance_mat <- dist(mtcars, method = 'euclidean')distance_mat# Fitting Hierarchical clustering Model # to training datasetset.seed(240) # Setting seedHierar_cl <- hclust(distance_mat, method = "average")Hierar_cl# Plotting dendrogramplot(Hierar_cl)# Choosing no. of clusters# Cutting tree by heightabline(h = 110, col = "green")# Cutting tree by no. of clustersfit <- cutree(Hierar_cl, k = 3 )fittable(fit)rect.hclust(Hierar_cl, k = 3, border = "green") |
Output:
- Distance matrix:
- The values are shown as per the distance matrix calculation with the method as euclidean.
- Model Hierar_cl:
- In the model, the cluster method is average, distance is euclidean and no. of objects are 32.
- Plot dendrogram:
- The plot dendrogram is shown with x-axis as distance matrix and y-axis as height.
- Cutted tree:
- So, Tree is cut where k = 3 and each category represents its number of clusters.
- Plotting dendrogram after cutting:
- The plot denotes dendrogram after being cut. The green lines show the number of clusters as per the thumb rule.
Hierarchical Clustering in R Programming
Hierarchical clustering in R Programming Language is an Unsupervised non-linear algorithm in which clusters are created such that they have a hierarchy(or a pre-determined ordering). For example, consider a family of up to three generations. A grandfather and mother have their children that become father and mother of their children. So, they all are grouped together to the same family i.e they form a hierarchy.