Clustering in R

In this module, you shall understand what machine learning is, why it is needed, supervised learning, its applications, unsupervised learning, and its applications. 

This section shall imbibe in you the knowledge of what clustering is, various distance measures in clustering, hierarchical clustering, and k-means clustering in brief. 

This section begins by explaining k-means clustering and understanding it through plots. It will then discuss the k-means algorithm, look at the working example, and its advantages in the latter part of this section.

The section begins by helping you understand the optimal number of clusters using R programming. It also demonstrates a code to work with k-means clustering later in this section. 

The section begins with a briefing on hierarchical clustering with cluster dendrogram. You will then gain knowledge on how to work with a problem statement for k-means with a demonstrated example to form a data cluster.