K-Means Clustering Implementation with Code Examples

This article presents a K-Means clustering implementation compatible with MATLAB 2019a, which can also be adapted for Python and other programming languages using relevant libraries. K-Means clustering is a fundamental unsupervised machine learning algorithm that partitions data points into a predetermined number of clusters (k) by iteratively minimizing the within-cluster sum of squares. The algorithm operates through these key steps: initialization of cluster centroids, assignment of data points to nearest centroids using Euclidean distance, and recalculation of centroids based on current cluster members. For implementation, the algorithm typically employs functions like kmeans() in MATLAB or sklearn.cluster.KMeans in Python's scikit-learn library. Key parameters include the number of clusters (k), maximum iterations, and convergence tolerance. The algorithm's efficiency makes it suitable for various applications including image segmentation (grouping similar pixels), text categorization (document clustering), and pattern recognition (feature grouping). Code implementation considerations involve proper data normalization, centroid initialization strategies (k-means++ recommended), and evaluation metrics such as silhouette score for cluster validation. The iterative nature of the algorithm ensures convergence to local optima, making multiple initializations advisable for optimal results.