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K-Means Clustering Q&A
1What is K-Means?
Answer: Unsupervised algorithm that partitions data into k clusters by minimizing within-cluster variance.
2How does it work?
Answer: Initialize centroids, assign points, update centroids, repeat until convergence.
3How choose k?
Answer: Elbow method, silhouette score, and domain understanding.
4Need feature scaling?
Answer: Yes, distance-based methods are sensitive to scale.
5What is inertia?
Answer: Sum of squared distances of points to their assigned centroid.
6K-Means assumptions?
Answer: Roughly spherical, similarly sized clusters with Euclidean distance suitability.
7What is K-Means++?
Answer: Smart centroid initialization to improve convergence and stability.
8Limitations?
Answer: Sensitive to outliers, initialization, and non-convex clusters.
9How evaluate clustering?
Answer: Silhouette score, Davies-Bouldin, and external labels if available.
10K-Means in one line?
Answer: Fast baseline clustering for compact, distance-friendly groups.