K Means Clustering, Hierarchical Clustering, Machine Learning, Dendrogram, Silhouette Score.
clustering: Hierarchical clustering can be computationally expensive, especially for large datasets. The time and memory requirements increase as the number of data points grows. Sensitivity to Initialization: K-means clustering: K-means is sensitive to the initial placement of centroids. Different