Why can the elbow method fail?
mediumAnswer
- (1) No clear elbow — smooth curve, subjective.
- (2) Different features scales dominate SSE → misleading k.
- (3) Sees the biggest cluster count as always better (SSE monotonically decreases).
- Use with silhouette or gap statistic as tiebreakers.
- In practice, teams combine 3 metrics + domain knowledge; blindly trusting elbow SSE is a common bug in production clustering pipelines.
Check yourself — multiple choice
- Never fails
- Smooth curve = subjective; feature scale dominates SSE; SSE monotone → higher k always looks better; use silhouette / gap as tiebreakers
- Same as silhouette
- Random
Elbow: subjective, scale-sensitive; combine with silhouette / gap statistic.
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