K-Means Clustering Algorithm

In the field of data analysis, the K-means clustering algorithm is a widely used unsupervised learning method for partitioning datasets into multiple clusters. The core principle involves grouping data points into clusters with similar attributes, where "K" represents the predetermined number of clusters. The algorithm implementation typically involves iterative centroid updates using MATLAB's kmeans() function, which handles distance calculations (Euclidean by default) and cluster reassignments until convergence.

Beyond general clustering applications, K-means serves effectively for key frame extraction in image and video processing. Here, the algorithm identifies representative frames by clustering visual features through histogram comparisons or feature vectors. MATLAB implementation utilizes image processing toolbox functions like imread() for frame loading and rgb2gray() for feature extraction, followed by kmeans() optimization with parameters specifying maximum iterations and tolerance thresholds. This approach enables automatic identification of frames most relevant to video themes and narrative structures.