26
Jan

K-Means is a widely used clustering algorithm that partitions a set of data points into K clusters, where each cluster is defined by its centroid. The goal of the algorithm is to minimize the sum of squared distances between each data point and its closest centroid. The algorithm starts by randomly selecting K initial centroids and assigning each data point to the closest centroid. Then, it iteratively updates the position of the centroids and reassigns each data point to the closest centroid until the assignments no longer change. The algorithm terminates when the centroids reach a stable position. Mathematical Intuition…