In today’s data-driven environment, clustering algorithms are of pivotal importance, especially when dealing with a large influx of unclassified data. One such renowned clustering algorithm from Data Science is Hierarchical Clustering. We'll be using R to illustrate how Hierarchical Clustering functions.
In the simplest words, the Hierarchical Clustering algorithm is an unsupervised clustering algorithm that can be visualized metaphorically as a tree or can be called a dendrogram. The algorithm starts by treating each data point as a single cluster, then it fuses the two most similar clusters and finally repeats this step until only one cluster remains. This method is primarily used to group identical data points into larger clusters.
Let's dive into some practical implementation of this algorithm using R.
# load required library library(cluster) # load data data = mtcars # eliminating categorical variable data$gear = NULL # scaling the variables data = scale(data) # computing distance dist = dist(data) # hierarchical clustering hclust = hclust(dist) plot(hclust)
In the code provided above, we have made use of the in-built 'mtcars' data available in R using the 'cluster' library. We have eliminated any categorical variables, such as the 'gear' column. For efficient functioning of the algorithm, we first need to scale our data, so each variable has equal weightage. We then compute the dissimilarities (using Euclidean distance metric).
With the distance found, we apply a hierarchical clustering algorithm on it, which is then plotted for visualization. The resulting dendrogram visually shows the merging of clusters.
Hierarchical Clustering has lots of practical usage in today's world such as fingerprint recognition, customer segmentation and more. Its simplicity, both in theory and practice coupled with its powerful visual representation, makes it a favorite among Data Scientists.
Note: Always remember to appropriately pre-process your data (like scaling) before subjecting it to any form of clustering to ensure the best results. These results, however, could be subjective depending on the business objectives at hand.
That's all for this quick introduction to Hierarchical Clustering in R. Happy learning!