Association Learning

What is Association Learning?

Association learning, often referred to in the context of association rule learning, is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. This method is widely used for market basket analysis, where it is used to find relationships between items that are frequently bought together.

The most famous example of association learning is the "beer and diapers" story, where a retail store supposedly discovered through data analysis that men often bought beer and diapers together. This story is anecdotal, but it illustrates the kind of insights that can be gained from association learning.

Key Concepts in Association Learning

Association learning is based on the concept of rules, which are implications of the form X → Y, where X and Y are disjoint itemsets. A typical association rule in a market basket analysis might state that if a customer buys bread and butter (X), they are likely to also buy milk (Y).

There are three key metrics used in association learning:

  • Support

    : This is the proportion of transactions in the database that contain the itemset. In other words, it's the probability that a transaction contains both X and Y.

  • Confidence: This is a measure of the reliability of the inference made by the rule. For a rule X → Y, it's the probability that a transaction containing X also contains Y.
  • Lift: This is the ratio of the observed support to that expected if X and Y were independent. A lift value greater than 1 indicates that the presence of X increases the likelihood that Y will also be present in the transaction.

Association Rule Learning Algorithms

There are several algorithms designed to efficiently find association rules in data. The most well-known of these are:

  • Apriori Algorithm: This algorithm identifies the itemsets that are frequently occurring (i.e., have support above a user-specified threshold) and then uses these itemsets to generate association rules that meet the confidence threshold.
  • Frequent Pattern (FP) Growth Algorithm: This is an improvement over the Apriori algorithm that uses a special data structure called an FP-tree to store the database in a compressed form. It is often faster than Apriori because it reduces the number of database scans.
  • Eclat Algorithm: This algorithm uses a depth-first search strategy to count the support of itemsets and uses a vertical database format where each item contains the list of transactions that contain that item.

Applications of Association Learning

Association learning has applications in various domains, including:

  • Retail: For market basket analysis to understand customer buying habits and to drive sales through promotions and store layout optimizations.
  • Healthcare: For identifying combinations of symptoms and diagnoses that frequently occur together, which can help in the diagnosis of new patients.
  • Web Usage Mining: For analyzing patterns in web usage data to improve website design and personalized content delivery.
  • Finance: For fraud detection by identifying unusual patterns of transactions.

Challenges in Association Learning

While association learning can be powerful, it also faces several challenges, such as:

  • Large Number of Rules: Association learning can produce a large number of rules, many of which may not be useful or could be redundant.
  • Setting Thresholds: Choosing the correct support and confidence thresholds can be difficult without domain knowledge.
  • Interpretation: The rules generated are purely statistical and do not necessarily imply causation.


Association learning is a valuable tool for uncovering hidden patterns in large datasets. It is particularly useful in domains where understanding the relationships between different items can lead to actionable insights. However, it requires careful interpretation and a good understanding of the domain to apply the findings effectively.


  • Agrawal, R., Imieliński, T., & Swami, A. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD international conference on Management of data (pp. 207-216).
  • Han, J., Pei, J., & Yin, Y. (2000). Mining frequent patterns without candidate generation. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 1-12).
  • Zaki, M. J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372-390.

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