Clustering is a supervised learning

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Question restatement: Clustering is a supervised learning. Answer options: True, False.
Option 1: True. This claim would imply that clustering relies on labeled data to guide the learning process. However, clustering is typically used to discover structure in data without predefined labels, which means it does not use supervision in the training phase. People might mistakenly think clustering is supervised because it yields grouped outputs, but the key distinction is the presence or absence of labeled training examples guiding the algorithm.
Option 2: False. The statement that clustering is supervised is incorrect because clustering aims to partition data into groups based on similarity without using external labels. In supervised learning, models are trained with input-output pairs (features and labels), whereas clustering operates in an unsupervised setting, seeking natural groupings or patterns in the data itself. This difference in the role of labels and guidance is the core reason why clustering is categorized as unsupervised rather than supervised.
Additional nuance: Some hybrid or semi-supervised approaches exist where partial labeling is used to influence clustering, but the canonical definition of clustering remains unsupervised. This helps distinguish it from bona fide supervised methods like classification or regression, where clear input-output mappings are provided during training.

Clustering is a supervised learning

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