K Means Clustering, Hierarchical Clustering, Machine Learning, Dendrogram, Silhouette Score.
Noise: K-means clustering does not handle noise or outliers well. It tends to assign noisy data points to the nearest cluster, potentially affecting the overall cluster structure and interpretability. One of the limitations of K-Means Clustering is that it assumes that the clusters are spherical