Your K-Means clustering algorithm is converging to a local minimum. What role might centroid initialization play in this, and how could you address it?
- Increase the number of clusters
- Initialize centroids based on labels
- Poor initialization; Try multiple random initializations
- Use a fixed number of centroids
Converging to a local minimum in K-Means is often due to poor initialization. Running the algorithm multiple times with different random initializations can help avoid local minima and lead to a more globally optimal solution.
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