In the k-Means algorithm, after current iteration, we have 3 centroids (0, 1) (2, 1), (-1,...

Transcribed Image Text:

In the k-Means algorithm, after current iteration, we have 3 centroids (0, 1) (2, 1), (-1, 2). Then the points (2, 3) and (2, 0.5) be assigned to the same cluster in the next iteration. True for Manhattan distance, but not for Euclidean distance True, if Manhattan or Euclidean distances are used. True for Euclidean distance, but not true for Manhattan distance. O Not true for both Manhattan and Euclidean distances. In the k-Means algorithm, after current iteration, we have 3 centroids (0, 1) (2, 1), (-1, 2). Then the points (2, 3) and (2, 0.5) be assigned to the same cluster in the next iteration. True for Manhattan distance, but not for Euclidean distance True, if Manhattan or Euclidean distances are used. True for Euclidean distance, but not true for Manhattan distance. O Not true for both Manhattan and Euclidean distances.

Answer rating: 100% (QA)

The detailed answer for the above question is provided below The correct answer is True for Manhatta... View the full answer