Which of the following can act as possible termination conditions in K-Means? 1. For a fixed number of iterations. 2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum. 3. Centroids do not change between successive iterations. 4. Terminate when RSS falls below a threshold.

1, 3 and 4
1, 2 and 3
1, 2 and 4
1,2,3,4

The correct answer is: D. 1,2,3,4

All of the options can act as possible termination conditions in K-Means.

  1. For a fixed number of iterations: This is a common termination condition, as it is often desirable to have a fixed number of iterations 64 288 64S117.2 64 74.6 75.5c-23.5 6.3-42 24.9-48.3 48.6-11.4 42.9-11.4 132.3-11.4 132.3s0 89.4 11.4 132.3c6.3 23.7 24.8 41.5 48.3 47.8C117.2 448 288 448 288 448s170.8 0 213.4-11.5c23.5-6.3 42-24.2 48.3-47.8 11.4-42.9 11.4-132.3 11.4-132.3s0-89.4-11.4-132.3zm-317.5 213.5V175.2l142.7 81.2-142.7 81.2z"/> Subscribe on YouTube
to avoid overfitting.
  • Assignment of observations to clusters does not change between iterations: This is another common termination condition, as it indicates that the clusters have converged.
  • Centroids do not change between successive iterations: This is a less common termination condition, but it can be useful in cases where the clusters are very stable.
  • Terminate when RSS falls below a threshold: This is a more sophisticated termination condition, as it takes into account the quality of the clustering.
  • In general, it is best to use a combination of termination conditions to ensure that the K-Means algorithm converges to a good solution.

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