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Module 4: Clustering, K-means, K-medoids, Density-based Hierarchical, Spectral .

20222m

What is the minimum number of variables/features required to perform clustering?
(i) 0
(ii) 1
(iii) 2
(iv) 3

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Answer: (ii) 1

Explanation:

  • Clustering (K-means, hierarchical, DBSCAN) groups data points by similarity in feature space.
  • Requires at least 1 feature/variable to compute distance/similarity. With 0 features there is no data to cluster.
  • With 1 feature, 1D clustering is valid (e.g., points on a line clustered by value: {1,2,10} → clusters {1,2} and {10} via 1D K-means).
  • While typically we use ≥2\ge 2 features for visualization, minimum mathematically is 1.

Therefore (ii) 1 is correct.

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