MATLAB Implementation of K-Means Clustering Algorithm

In this article, we explore the implementation of the K-means clustering algorithm in MATLAB. K-means is a fundamental clustering algorithm designed to partition n objects into K clusters, maximizing intra-cluster similarity while minimizing inter-cluster similarity. The algorithm operates through iterative optimization: initial centroids are randomly selected, objects are assigned to nearest centroids using Euclidean distance, and centroids are recalculated until convergence criteria are met (e.g., centroid stabilization or maximum iterations).

The MATLAB implementation typically utilizes matrix operations for efficient distance calculations and employs functions like kmeans() from the Statistics and Machine Learning Toolbox. Alternatively, custom implementations can be created using loops or vectorization for centroid updates and cluster assignments. Beyond K-means, other clustering algorithms such as hierarchical clustering (using linkage functions) and density-based clustering (like DBSCAN) exist, allowing selection of optimal methods based on data characteristics like distribution density and cluster shapes for effective data partitioning.