Which statement correctly describes K-Means Clustering?

Options

  • A. It divides data into a specified number of clusters by assigning observations to the nearest cluster centroid
  • B. It requires every training example to have a predefined class label
  • C. It always creates a hierarchical tree of clusters
  • D. It is primarily used to predict continuous target values
  • E. None of the above

Correct Answer (Detailed Explanation is Below)

A. It divides data into a specified number of clusters by assigning observations to the nearest cluster centroid

Detailed Explanation

K-Means is an unsupervised clustering algorithm that divides data into K clusters. It generally assigns each observation to the nearest centroid and repeatedly updates the centroids until the clustering process converges or reaches a stopping condition.

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