K Means Clustering, Hierarchical Clustering, Machine Learning, Dendrogram, Silhouette Score.
distinct boundaries. The algorithm aims to minimize the within-cluster sum of squares, making it effective in identifying compact and spherical clusters. Large Datasets: K-means clustering is computationally efficient and can handle large datasets with a reasonable number of clusters. It scales