What is the main purpose of association rules in data analysis?

The main purpose of association rules in data analysis is to identify relationships between attributes within large datasets. These rules help in discovering patterns and correlations in the data, revealing how the presence of one item may be associated with the presence of another. For instance, in a retail context, association rules might highlight that customers who buy bread are also likely to buy butter.

This analysis can be particularly beneficial for making data-driven decisions, enhancing marketing strategies, or cross-selling products. By focusing on relationships rather than just aggregating data, association rules allow organizations to gain insights that can drive business or research decisions.

The other options focus on different aspects of data management and governance, such as integrity, security, and backups, which do not align with the primary role of association rules. Therefore, identifying relationships between attributes is central to the purpose of association rules in data analysis.