Definition
| transaction ID | milk | bread | butter | beer |
|---|---|---|---|---|
| 1 | 1 | 1 | 0 | 0 |
| 2 | 0 | 0 | 1 | 0 |
| 3 | 0 | 0 | 0 | 1 |
| 4 | 1 | 1 | 1 | 0 |
| 5 | 0 | 1 | 0 | 0 |
Following the original definition by Agrawal et al. the problem of association rule mining is defined as: Let be a set of binary attributes called items. Let be a set of transactions called the database. Each transaction in has a unique transaction ID and contains a subset of the items in . A rule is defined as an implication of the form where and . The sets of items (for short itemsets) and are called antecedent (left-hand-side or LHS) and consequent (right-hand-side or RHS) of the rule respectively.
To illustrate the concepts, we use a small example from the supermarket domain. The set of items is and a small database containing the items (1 codes presence and 0 absence of an item in a transaction) is shown in the table to the right. An example rule for the supermarket could be meaning that if butter and bread are bought, customers also buy milk.
Note: this example is extremely small. In practical applications, a rule needs a support of several hundred transactions before it can be considered statistically significant, and datasets often contain thousands or millions of transactions.
Read more about this topic: Association Rule Learning
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