Understanding Clusters and Centroids in k-means Clustering

This chapter explores the concept of clusters and centroids in the k-means clustering algorithm, discussing how clusters are groups of data points and centroids are the middle points of clusters. It also covers classifying new data points based on their proximity to centroids and provides insights into determining the number of clusters and evaluating their quality.