Which description best matches cluster analysis?

Which description best matches cluster analysis?

• A. Cluster analysis assigns members of a dataset into categories based on known characteristics.
• B. Cluster analysis explores data to find groups with common and previously unknown characteristics.
• C. Cluster analysis predicts a numerical value given characteristics of each member of a dataset.
• D. Cluster analysis develops algorithms to develop rules to apply to new data.

Answer: (B)

Cluster analysis is an unsupervised learning approach that groups data by similarity, without using predefined labels. Its goal is to uncover natural groupings in the data and to reveal common characteristics that weren't known in advance. This aligns with exploring data to find groups with common and previously unknown characteristics. Other descriptions describe supervised methods: assigning into categories based on known characteristics is classification; predicting a numerical value is regression; and developing rules to apply to new data is typical of supervised rule-based modeling such as decision trees. Clustering does not rely on labeled outcomes, only on the pattern of attribute similarity.

Cluster analysis is an unsupervised learning approach that groups data by similarity, without using predefined labels. Its goal is to uncover natural groupings in the data and to reveal common characteristics that weren't known in advance. This aligns with exploring data to find groups with common and previously unknown characteristics.

Other descriptions describe supervised methods: assigning into categories based on known characteristics is classification; predicting a numerical value is regression; and developing rules to apply to new data is typical of supervised rule-based modeling such as decision trees. Clustering does not rely on labeled outcomes, only on the pattern of attribute similarity.