Association rules belong to the family of unsupervised learning methods. Their goal is to reveal meaningful relationships in large datasets, without relying on a predefined target variable to predict. These rules take the form of simple relationships such as “if A, then B” or “if A and B, then C.”

The logic is intuitive: if events frequently occur together in the data, it is possible to deduce useful links to better understand behaviors or optimize decisions.

In retail, this technique is often illustrated by market basket analysis. For example, noticing that a customer who buys coffee often also buys sugar. But the value of association rules goes far beyond this case: they are used in finance to detect fraud patterns, in digital marketing to personalize recommendations, and even in cybersecurity to identify suspicious combinations of actions on a network.

How association rules work

The basic idea is simple: a large volume of transactions contains many possible combinations, but not all are interesting. The first step is therefore to identify the products or events that appear most frequently. Only these frequent occurrences are retained for further analysis.

From these frequent elements, the algorithm progressively builds broader associations. For example, if coffee purchases are common and sugar purchases are also common, the algorithm will check whether the combination “coffee and sugar” appears regularly in the data. This iterative logic, called “Apriori,” avoids testing millions of rare and less useful combinations.

Once candidate associations are identified, their strength is measured. Confidence indicates the proportion of times the consequence actually occurs when the antecedent is observed. In our example, it measures the probability that a basket containing coffee also contains sugar. Lift provides a complementary check: it compares this probability with what would happen if the two events were completely independent. A lift greater than 1 shows that the association is truly significant.

Association rules offer several major advantages. They are intuitive to understand and easy to communicate to decision-makers, since they take the form of simple relationships like “if A, then B.” They also help unlock value from transactional databases, which accumulate quickly in information systems, by revealing patterns that escape human analysis.

However, some precautions are necessary. The quality of the results strongly depends on the threshold chosen to filter rules: a threshold too low generates a multitude of weak associations, while a threshold too high may eliminate interesting patterns. Furthermore, an association does not prove causation. The fact that two products are often purchased together does not mean one causes the other; it may simply reflect consumption habits.

Finally, practical implementation requires sufficient computational capacity. In datasets with thousands of products or actions, the number of possible combinations grows rapidly. Companies must therefore rely on specialized tools and properly size their infrastructure to use this technique effectively.