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Association rule learning

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.In 1992, Thomas Blischok, manager of a retail consulting group at Teradata, and his staff prepared an analysis of 1.2 million market baskets from about 25 Osco Drug stores. Database queries were developed to identify affinities. The analysis 'did discover that between 5:00 and 7:00 p.m. that consumers bought beer and diapers'. Osco managers did NOT exploit the beer and diapers relationship by moving the products closer together on the shelves. 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. Based on the concept of strong rules, Rakesh Agrawal, Tomasz Imieliński and Arun Swami 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 { o n i o n s , p o t a t o e s } ⇒ { b u r g e r } {displaystyle {mathrm {onions,potatoes} }Rightarrow {mathrm {burger} }} 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. Following the original definition by Agrawal, Imieliński, Swami the problem of association rule mining is defined as: Let I = { i 1 , i 2 , … , i n } {displaystyle I={i_{1},i_{2},ldots ,i_{n}}} be a set of n {displaystyle n} binary attributes called items. Let D = { t 1 , t 2 , … , t m } {displaystyle D={t_{1},t_{2},ldots ,t_{m}}} be a set of transactions called the database. Each transaction in D {displaystyle D} has a unique transaction ID and contains a subset of the items in I {displaystyle I} . A rule is defined as an implication of the form: X ⇒ Y {displaystyle XRightarrow Y} , where X , Y ⊆ I {displaystyle X,Ysubseteq I} .

[ "Algorithm", "Machine learning", "Data mining", "Artificial intelligence", "Apriori algorithm", "distributed mining", "parallel mining", "inductive database", "association rule discovery" ]
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