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Answer: Centroids do not change between iterations., The residual sum of squares (RSS) falls below a threshold., A fixed number of iterations is executed.
The K-means algorithm terminates based on convergence criteria or predefined limits. Option A (Centroids do not change between iterations) indicates convergence when cluster assignments stabilize. Option C (The residual sum of squares (RSS) falls below a threshold) reflects the algorithm's goal to minimize RSS, stopping when improvement is minimal. Option D (A fixed number of iterations is executed) is a common safeguard to prevent infinite loops. Option B is incorrect because RSS rising contradicts the minimization objective. Option E is invalid as K-means does not maximize centroid distances. The community discussion, with high upvotes and references to Azure documentation, confirms A, C, and D as the correct termination conditions.
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You are using the K-means algorithm to perform clustering. You need to define the possible stopping conditions for the algorithm. Which three conditions can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.
A
Centroids do not change between iterations.
B
The residual sum of squares (RSS) rises above a threshold.
C
The residual sum of squares (RSS) falls below a threshold.
D
A fixed number of iterations is executed.
E
The sum of distances between centroids reaches a maximum.
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