|
Association rule learning is a method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using different measures of interestingness.〔Piatetsky-Shapiro, Gregory (1991), ''Discovery, analysis, and presentation of strong rules'', in Piatetsky-Shapiro, Gregory; and Frawley, William J.; eds., ''Knowledge Discovery in Databases'', AAAI/MIT Press, Cambridge, MA.〕 Based on the concept of strong rules, Rakesh Agrawal et al. introduced association rules for discovering regularities between products in large-scale transaction data recorded by point-of-sale (POS) systems in supermarkets. For example, the rule found in the sales data of a supermarket would indicate that if a customer buys onions and potatoes together, they are likely to also buy hamburger meat. Such information can be used as the basis for decisions about marketing activities such as, e.g., promotional pricing or product placements. In addition to the above example from market basket analysis association rules are employed today in many application areas including Web usage mining, intrusion detection, Continuous production, and bioinformatics. In contrast with sequence mining, association rule learning typically does not consider the order of items either within a transaction or across transactions. == Definition == 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 . Every rule is composed by two different set of items, also known as ''itemsets'', and , where is called ''antecedent'' or left-hand-side (LHS) and ''consequent'' or right-hand-side (RHS). To illustrate the concepts, we use a small example from the supermarket domain. The set of items is and in the table is shown a small database containing the items, where, in each entry, the value 1 means the presence of the item in the corresponding transaction, and the value 0 represent the absence of an item in a that transaction. 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 data-sets often contain thousands or millions of transactions. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「association rule learning」の詳細全文を読む スポンサード リンク
|