In K-Means clustering, a common approach to avoid local minima due to initial centroid selection is to run the algorithm multiple times with different _________.
- Centroid initializations
- Distance metrics
- Learning rates
- Number of clusters
Running the K-Means algorithm multiple times with different centroid initializations helps in avoiding local minima. It increases the chance of finding a more globally optimal clustering solution.
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