Which data mining activity involves organizing data into predefined classes?

Classification is the data mining activity that involves organizing data into predefined classes. In this process, a model is built using training data that is already labeled with the correct class. The goal of classification is to accurately predict the class of new, unseen instances based on the learned patterns in the training data. This technique is commonly used in various applications such as spam detection in email systems, sentiment analysis in reviews, and classification of medical diagnoses.

Clustering, on the other hand, groups data into clusters based on similarities without predefined labels, making it a different task from classification. Estimation is typically concerned with predicting a continuous outcome rather than categorizing into classes. Integration refers to combining data from different sources, which, while important in data management, does not involve the organization of data into predefined categories like classification does.