The Elbow Method is a heuristic used in clustering algorithms to determine the optimal number of clusters by plotting the explained variance as a function of the number of clusters and looking for the point where adding more clusters yields diminishing returns. This 'elbow' point indicates a suitable balance between model complexity and performance, helping to avoid overfitting while ensuring meaningful groupings within the data.
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