K Means Clustering and Unsupervised Learning Questions
Unsupervised learning finding patterns without labels. K-Means: algorithm for partitioning data into k clusters by iteratively assigning points to nearest cluster center and updating centers. For junior level, explain the algorithm steps, how to choose k (elbow method), distance metrics (Euclidean, Manhattan), advantages (simple, fast) and disadvantages (sensitive to initialization, assumes spherical clusters). Understand other approaches like hierarchical clustering and DBSCAN conceptually.
MediumTechnical
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Explain kernel K Means conceptually. Describe how the kernel trick enables clustering in implicit higher-dimensional feature spaces, how distances are computed using the Gram matrix, and discuss the computational and memory trade-offs compared to standard K Means.
EasyTechnical
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Why is feature scaling important before applying K Means? Provide a concrete numeric example where unscaled features cause poor clustering, and describe scaling techniques you would apply in practice such as standard scaling, min max, or robust scaling.
MediumTechnical
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Explain internal versus external clustering validation metrics. Provide examples such as silhouette, Davies Bouldin, Calinski Harabasz for internal metrics and adjusted rand index and mutual information for external metrics. Discuss strengths, weaknesses, and when each family is appropriate.
HardTechnical
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Implement or outline an algorithm to match clusters across two clusterings for very large datasets (millions of points). Describe building a contingency matrix, using the Hungarian algorithm for exact matching, and a memory-efficient streaming approach to compute contingency counts plus an approximate greedy alternative.
EasyTechnical
0 practiced
Explain hierarchical clustering including agglomerative and divisive approaches, common linkage criteria (single, complete, average), and how to interpret a dendrogram. Compare hierarchical clustering tradeoffs versus K Means in terms of complexity and suitability.
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