K Means Clustering, Hierarchical Clustering, Machine Learning, Dendrogram, Silhouette Score.
distribution. Cons: Sensitivity to Initialization: K-means clustering is sensitive to the initial placement of centroids. Different initializations can lead to different clustering results, and the algorithm may converge to sub-optimal solutions. Fixed Number of Clusters: K-means clustering