k-means Algorithm Implementation with MATLAB Code

This document discusses the k-means clustering algorithm implementation. The algorithm takes parameter k as input and partitions n data objects into k distinct clusters. The implementation ensures that objects within the same cluster exhibit high similarity while maintaining low similarity between different clusters. Cluster similarity computation utilizes centroid objects (gravity centers) derived from calculating the mean values of all objects within each cluster. The MATLAB source code provided implements key algorithm components including: - Initial centroid selection using random sampling or k-means++ initialization - Iterative Lloyd's algorithm with assignment and update steps - Euclidean distance calculation for object-to-centroid comparisons - Convergence detection based on centroid movement thresholds For testing purposes, the implementation uses the Iris flower dataset, which contains 150 samples with 4 features each. Users can modify the code to experiment with different datasets and parameters to better understand clustering behavior and algorithm performance. The code includes visualization functions to plot cluster boundaries and centroid movements during iteration.