The elbow method is a technique used to determine the optimal number of clusters in a dataset during the clustering process. This method involves plotting the explained variance as a function of the number of clusters and identifying the point where adding more clusters yields diminishing returns, resembling an elbow shape in the graph. This visual cue helps to balance model complexity with performance, guiding decisions in unsupervised learning.
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