Implementation of K-Means Clustering Algorithm with Code Description

This experiment implements the K-means clustering algorithm. The principle involves identifying C cluster centroids from training samples, where each centroid represents the center of a class. Samples are then assigned to the class corresponding to their nearest centroid. The value of C is selected based on prior knowledge or empirical data, while cluster centroids are computed iteratively through the algorithm. A typical implementation includes random centroid initialization, distance calculation using metrics like Euclidean distance, and iterative centroid updates until convergence.