1. Clustering Algorithms 1.1 K-Means An iterative algorithm that partitions n observations into k clusters, where each observation belongs to the cluster with the nearest mean. Use Cases: Customer segmentation Image compression Document clustering Anomaly detection Pattern recognition Strengths: Simple to understand and implement Scales well to large datasets Fast convergence Memory efficient Works well…
