The K means algorithm is an unsupervised machine learning algorithm. Unsupervised learning is another way of classifying the data as it does not require labeling of the data. In reality, there are many instances where labeling of the data is not possible, so we require them to classify data based on unsupervised learning. Unsupervised learning uses the similarity between data elements and assigns each data point to its relevant cluster. Each cluster has a set of data points which are similar in nature. The K means algorithm is the most basic unsupervised learning algorithm and it just requires data to plug into the algorithm along with the number of clusters we would like it to cluster returning the vector of cluster labeling for each data point. I used normalized data along with the number of clusters. I used the in-sample data which was used during logistic regression, to be divided into three clusters.
set.seed() is used to have the same output in every iteration; without...