The elbow method is a heuristic used to determine the optimal number of clusters in a dataset when using clustering algorithms like K-means. This technique involves plotting the explained variance against the number of clusters and looking for a point where the rate of improvement sharply declines, resembling an 'elbow.' This helps in identifying the most suitable number of clusters that balance complexity and interpretability.
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