The K-means algorithm
K-means is one of the most popular unsupervised algorithms for data clustering, which is used when we have unlabeled data without defined categories or groups. The number of clusters is represented by the k variable. This is an iterative algorithm that assigns the data points to a specific cluster based on the distance from the arbitrary centroid. During the first iteration, the centroids are randomly defined and the data points are assigned to the cluster based on the least vicinity from the centroid. Once the data points are allocated, within the subsequent iterations, the centroids are realigned to the mean of the data points and the data points are once again added to the clusters based on the least vicinity from the centroids. These steps are iterated to the point where the centroids do not change more than the set threshold. Let's illustrate the K-means algorithm with three iterations on a sample two dimensional (x1, x2) dataset:
Iteration 1:
- During the first iteration...
