Which data mining method is used to group data into clusters without predefined labels?

Which data mining method is used to group data into clusters without predefined labels?

• A. Machine learning
• B. Association rule learning
• C. Classification
• D. Clustering

Answer: (D)

Grouping data into clusters without predefined labels is the job of clustering, an unsupervised data mining approach. It looks for natural groupings by measuring how similar data points are to each other and assigns points to clusters so that members of a cluster are more alike than to members of other clusters. No prior labels are needed, which is why clustering is used when the data is unlabeled and the goal is to discover structure in the data. Common methods include k-means, hierarchical clustering, and density-based clustering like DBSCAN. By contrast, classification requires known labels to train a model; association rule learning discovers relationships between items in transactional data; machine learning is the broader field that includes both supervised and unsupervised methods.

Grouping data into clusters without predefined labels is the job of clustering, an unsupervised data mining approach. It looks for natural groupings by measuring how similar data points are to each other and assigns points to clusters so that members of a cluster are more alike than to members of other clusters. No prior labels are needed, which is why clustering is used when the data is unlabeled and the goal is to discover structure in the data. Common methods include k-means, hierarchical clustering, and density-based clustering like DBSCAN. By contrast, classification requires known labels to train a model; association rule learning discovers relationships between items in transactional data; machine learning is the broader field that includes both supervised and unsupervised methods.