MATLAB Implementation of K-Means Clustering Algorithm

This project involves implementing a K-means clustering algorithm program to perform cluster analysis on the data shown in the figure below, with k=2 selected. In cluster analysis, the K-means algorithm is a commonly used unsupervised learning method that partitions a dataset into k non-overlapping clusters. The fundamental principle of this algorithm involves iterative computation that continuously updates the centroid of each cluster until the centroids stabilize and no longer change significantly. The implementation typically involves several key steps: initial centroid selection (often using random initialization or k-means++ method), assigning data points to the nearest centroid based on Euclidean distance, and recalculating centroids as the mean of all points in each cluster. These steps repeat until convergence criteria are met, such as maximum iterations reached or centroid movement below a specified threshold. For this experiment, we have chosen k=2, meaning the dataset will be partitioned into two distinct clusters. By programming the K-means clustering algorithm, we can analyze the clustering patterns in the illustrated data, which helps reveal underlying data characteristics and distribution properties. The MATLAB implementation would include functions for data loading, distance calculation, cluster assignment, and centroid update, with visualization components to display the final clustering results and convergence progression.