Which of the following statements about clustering in machine learning is true?

Question

BlackTom AI · Accepted Answer

Let’s examine the question: Which of the following statements about clustering in machine learning is true?

Option 1: 'Clustering is a supervised learning technique that requires labeled data.' This is incorrect. Clustering is typically an unsupervised learning method that does not rely on labeled data to form groups; labels are not used to define clusters during the learning process.

Option 2: 'Clustering tries to achieve high similarity between clusters and low similarity within each cluster.' This statement flips the intended goal. In clustering, we want high similarity among items within the same cluster (cohesion) and low similarity between items in different clusters (separation). Therefore, this option describes an inverted objective and is false.

Option 3: 'Clustering randomly assigns data points to ensure cluster diversity.' Random assignment ignores the data’s structure and patterns, which defeats the purpose of clustering. Clustering algorithms seek to group data based on inherent similarities, not random placement, so this is false.

Option 4: 'Clustering assigns data to groups based on patterns in the data, while classification assigns data to predefined labels using a trained model.' This accurately contrasts clustering (unsupervised grouping by patterns) with classification (supervised labeling by a trained model). This option correctly captures the fundamental distinction between the two tasks in machine learning, making it true.

In summary, the correct understanding is that clustering groups data by intrinsic patterns without labels, whereas classification uses labeled data to map inputs to predefined categories. The remaining options misstate the learning paradigm, the objective, or the role of labels in clustering.

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Which of the following statements about clustering in machine learning is true?

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