K-means Clustering

The document discusses the K-means clustering algorithm, an unsupervised learning technique that groups unlabeled data points into K clusters based on their similarities. It works by randomly initializing K cluster centroids and then iteratively assigning data points to their nearest centroid and recalculating the centroid positions based on the new assignments until convergence is reached. The document notes that K-means clustering requires specifying the number of clusters K in advance and presents the elbow method as a way to help determine an appropriate value of K for a given dataset.