What is the main goal of association rule learning?

The main goal of association rule learning is to discover relations between variables. This technique is commonly used in data mining to identify patterns and relationships within large datasets, particularly in transactional data. For instance, association rule learning can reveal insights like "customers who purchase bread also tend to buy butter," which helps businesses understand consumer behavior and improve marketing strategies.

By discovering these relationships, organizations can make data-driven decisions and develop strategies based on the patterns found in the data. This is particularly valuable in fields such as market basket analysis, recommendation systems, and any context where understanding the interaction between different variables can lead to actionable insights.

In contrast, the other options do not align with the primary objective of association rule learning. Classifying data into categories typically pertains to classification tasks, visualizing data trends focuses on representation rather than discovery of relationships, and cleaning the dataset involves preprocessing rather than analysis of associations.