Data Mining Practice Test

The main idea here is labeling items into fixed categories. Classification is a supervised learning task where the model is trained on examples that already have category labels and then learned to assign new items to one of those predefined classes. The outputs are discrete categories, not numeric values, which fits exactly when you want each item to belong to a specific label like “spam” or “not spam,” “cat” or “dog,” or any other set of categories. Clustering, by contrast, groups data without predefined labels, seeking natural clusters rather than assigning to known categories. Association focuses on discovering relationships and rules between items (like if one item appears with another), not on categorizing items into labeled classes. Prediction can be broader, but when the task is about assigning to fixed categories, classification is the precise fit.