When using K-means clustering, why is it sometimes recommended to run the algorithm multiple times with different initializations?
- To ensure deterministic results.
- To make the algorithm run faster.
- To mitigate sensitivity to initial cluster centers.
- To reduce the number of clusters.
K-means clustering is sensitive to initial cluster centers. Running it multiple times with different initializations helps find a more stable solution.
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