K Means Clustering, Hierarchical Clustering, Machine Learning, Dendrogram, Silhouette Score.
requires the number of clusters (k) to be specified in advance. Determining the optimal number of clusters can be challenging and may require trial and error or additional validation techniques. Sensitive to Outliers: K-means clustering treats all data points equally and is sensitive to outliers.