You've applied K-Means clustering, but the results are inconsistent across different runs. What could be the issue, and how would you address it?
- Change Number of Clusters
- Increase Dataset Size
- Initialize Centroids Differently
- Use Different Distance Metric
K-Means clustering can be sensitive to initial centroid placement. Trying different initialization strategies can lead to more consistent results.
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