MATLAB Implementation of K-Means Clustering Algorithm

The implementation employs the K-means clustering algorithm, a widely used unsupervised machine learning method that partitions datasets into k distinct clusters. The algorithm ensures that data points within the same cluster exhibit higher similarity while maintaining greater separation between different clusters. In the code implementation, I incorporated essential preprocessing steps including data cleaning and normalization to meet algorithmic requirements. The core functionality handles centroid initialization, iterative distance calculations using Euclidean metrics, and cluster reassignment until convergence criteria are met. Through multiple test cycles with various datasets, I have verified the code's correctness and robustness. For those interested in K-means algorithm implementation, this code provides a clear MATLAB example with commented sections explaining key operations like cluster centroid updates and convergence checking. I welcome users to download and utilize this implementation, and I'm open to discussing the technical approach and implementation details.