MATLAB Source Code for K-means Clustering Algorithm with Multidimensional Data Support

This article presents a newly developed MATLAB source code implementation of the K-means clustering algorithm suitable for multidimensional data analysis. The algorithm facilitates effective data clustering analysis, enabling the identification of underlying patterns and relationships within datasets. K-means is a widely-used unsupervised clustering algorithm that partitions data into K predefined clusters based on centroid optimization. The implementation includes key MATLAB functions such as kmeans() for core clustering operations, pdist() for distance calculations, and randperm() for initial centroid selection. The code features an iterative optimization process that minimizes within-cluster variance through Euclidean distance measurements and centroid repositioning. Through this algorithm implementation, researchers can perform comprehensive data analysis to better understand dataset structures, leading to more accurate and meaningful decision-making in pattern recognition and data mining applications. The code incorporates efficient matrix operations and vectorized computations for optimal performance with high-dimensional datasets.