The elbow plot helps determine the optimal number of clusters by looking for:

Question

BlackTom AI · Accepted Answer

When evaluating how the elbow plot is used in clustering, it’s helpful to walk through what each option implies and contrast it with the actual purpose.
Option 1: 'The number of clusters where all points are perfectly separated.' In practice, clustering does not guarantee perfect separation of every point for any finite number of clusters, and elbow plots don’t optimize for perfect separation; they focus on stability of within-cluster variance as clusters increase.
Option 2: 'The point where adding more clusters no longer significantly reduces within-cluster variance.' This description aligns with the elbow method: you look for a diminishing return in reducing within-cluster variance (or sum of squared errors) as clusters increase, and the “elbow” indicates a preferable trade-off between compactness and simplicity.
Option 3: 'The cluster count where all points merge into a single group.' This would correspond to using only one cluster, which is the opposite of the purpose of choosing an elbow point; the elbow method seeks an intermediate number of clusters, not the minimum one.
Option 4: 'The cluster number with the highest variance.' The elbow method does not aim to maximize variance; it seeks a balance where additional clusters no longer substantially improve compactness, i.e., variance reduction slows down, not a peak in variance.
In summary, the elbow plot is a heuristic that identifies where incremental clustering gains (in terms of reducing within-cluster variance) begin to diminish, guiding the selection of a reasonable number of clusters rather than pursuing perfect separation, single-cluster dominance, or maximal variance. Different options misstate that goal in various ways, either by implying perfect separation, complete merging, or maximizing variance.

The elbow plot helps determine the optimal number of clusters by looking for:

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