What is clustering in machine learning and how does it differ from classification?

Updated May 15, 2026

Short answer

Clustering is an unsupervised learning technique that groups similar data points, while classification is supervised and uses labeled data.

Deep explanation

Clustering identifies hidden patterns in unlabeled data by grouping similar points based on distance or similarity metrics. Classification, on the other hand, learns from labeled datasets to assign predefined categories. Clustering is exploratory, while classification is predictive.

Real-world example

Customer segmentation in marketing without predefined labels.

Common mistakes

Assuming clustering requires labeled data like classification.

Follow-up questions

Can clustering be used for prediction?

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