Explain K-Means Clustering.

Explain K-Means Clustering.

Approach: Describe the iterative process of centroid assignment and update.

An unsupervised algorithm that partitions data into K distinct clusters. It initializes K centroids, assigns each point to the nearest centroid, and then recalculates the centroids by taking the mean of all points assigned to that cluster. This repeats until convergence.

Follow-up: How do you choose the value of K?

Key concepts: centroids, euclidean distance, inertia.