In ULaMDyn, several algorithms are available for clustering analysis, each differing in its interpretation of what constitutes a cluster and how clusters are identified. The clustering algorithms provided include: • K-Means Clustering: This algorithm partitions data into a predefined number of clusters by minimizing the sum of squared distances between data points and their respective cluster centroids. Because K-Means assumes an isotropic data distribution per cluster (equal variance in all directions), this method tends to work better on datasets with inherent globular or spherical cluster shapes. Each data point is assigned to the nearest cluster in a rigid, nonoverlapping manner, resulting in distinct Voronoi cells that define the cluster boundaries .