K-means clustering is a widely-used machine-learning algorithm designed to partition a dataset into K distinct clusters. Each data point is allocated to the cluster with the nearest centroid (center), with the objective of aggregating similar data points. The algorithm iteratively refines the cluster assignments and centroids until it reaches a state of convergence.
First proposed by Stuart Lloyd in 1957, K-means clustering was initially developed to solve the issue of pulse-code modulation in telecommunications. Owing to its simplicity and efficacy in clustering data, this algorithm has gained significant popularity across diverse fields.
K-means clustering is a data-grouping technique that endeavors to cluster data points in a manner that maximizes the similarity among points within the same cluster. It accomplishes this by minimizing the within-cluster variance, essentially bringing the points within each cluster as close together as possible. The centroid of a cluster serves as a reference point, and data points are assigned to the cluster whose centroid is closest to them. The key interpretation lies in the understanding that points belonging to the same cluster exhibit a higher degree of similarity to one another compared to points in other clusters.