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Module 4: Cluster Analysis

20262m

A major limitation of K-means is

(i) It cannot process numerical data
(ii) It always identifies outliers correctly
(iii) It requires the number of clusters to be specified in advance
(iv) It does not use distance measures

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Solution

Correct Answer: (iii) It requires the number of clusters to be specified in advance

Explanation

K-means needs the user to give k, the number of clusters, before it runs. A wrong k gives poor clusters, and the right k is usually unknown. Other limitations: sensitive to outliers, depends on initial centroids, finds only spherical clusters, works on numeric data.

Why other options are incorrect:

  • (i) K-means is designed for numerical data.
  • (ii) It does not handle outliers well; they pull centroids away.
  • (iv) It relies on a distance measure (usually Euclidean).

Answer: (iii) It requires the number of clusters to be specified in advance

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