Which data mining technique is best for grouping individuals by similarity without prior labeling?

Which data mining technique is best for grouping individuals by similarity without prior labeling?

• A. Association rule learning
• B. Regression
• C. Cluster analysis
• D. Classification

Answer: (C)

Clustering groups data by similarity without using any predefined labels, making it the natural choice when you want to find natural, unlabeled groupings among individuals. This unsupervised approach assesses how close or related items are to one another and assigns them to clusters so that members within a cluster are more alike than those in different clusters. It’s especially useful for tasks like customer segmentation or spotting hidden subtypes, because you’re letting the data reveal its own structure rather than forcing it into preexisting categories. You can apply methods that suit the data shape, such as k-means for well-separated groups, hierarchical clustering for nested structures, or density-based methods for irregular clusters, and you can evaluate cluster quality with metrics like silhouette width to gauge cohesion and separation. Other techniques don’t fit the goal here: association rule learning seeks interesting relationships among items rather than grouping individuals; regression predicts a numeric outcome from inputs; and classification assigns items to predefined labels based on labeled training data.

Clustering groups data by similarity without using any predefined labels, making it the natural choice when you want to find natural, unlabeled groupings among individuals. This unsupervised approach assesses how close or related items are to one another and assigns them to clusters so that members within a cluster are more alike than those in different clusters. It’s especially useful for tasks like customer segmentation or spotting hidden subtypes, because you’re letting the data reveal its own structure rather than forcing it into preexisting categories. You can apply methods that suit the data shape, such as k-means for well-separated groups, hierarchical clustering for nested structures, or density-based methods for irregular clusters, and you can evaluate cluster quality with metrics like silhouette width to gauge cohesion and separation.

Other techniques don’t fit the goal here: association rule learning seeks interesting relationships among items rather than grouping individuals; regression predicts a numeric outcome from inputs; and classification assigns items to predefined labels based on labeled training data.