What does the term 'association rule' refer to in data mining?

The term 'association rule' in data mining specifically refers to a rule that highlights relationships or correlations between variables within a dataset. This concept is fundamental in discovering patterns where the presence of one item in a transaction is associated with the presence of another item. For example, in market basket analysis, association rules can reveal that when customers buy bread, they are likely to buy butter as well.

The strength of these rules is typically measured by metrics such as support and confidence, which help in determining how often items co-occur and the likelihood of purchasing one item given the presence of another. This capability makes association rules particularly valuable for making recommendations and understanding customer behavior.

While data quality, data cleaning, and clustering are important aspects of data analysis, they do not encapsulate the essence of what association rules signify. Instead, they serve different purposes within the broader data mining process, focusing on ensuring the integrity of data, preparing data for analysis, and grouping similar data points, respectively.