K-Means Image Segmentation Algorithm Implementation in MATLAB

Implementation of K-means clustering algorithm for image segmentation in MATLAB. The algorithm accepts parameter k as input and partitions n data objects into k clusters, ensuring high similarity within clusters and low similarity between different clusters. Cluster similarity is calculated using centroids (gravitational centers) obtained from the mean values of objects in each cluster.

k-means Algorithm Implementation with MATLAB Code

The k-means algorithm accepts parameter k as input and partitions n data objects into k clusters, ensuring high similarity within clusters and low similarity between clusters. Cluster similarity is calculated using centroid objects (gravity centers) obtained from the mean values of objects in each cluster. This MATLAB implementation uses the Iris dataset for testing and demonstrates centroid calculation, iterative assignment, and convergence mechanisms.