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Author: Admin | 2025-04-28
Rule mining refers to a relationship between diapers and beer. The example, which seems fictional, claims that men who go to a store to buy diapers are also likely to buy beer. The association rule for this example is expressed as a percentage, indicating the likelihood a customer buys beer and diapers together. Data that points to that might look like this: A supermarket has 200,000 customer transactions in a week. Of the total number of transactions, 4,000, or 2%, include the purchase of diapers. 5,500 transactions, or 2.75%, include the purchase of beer. 3,500 transactions, or 1.75%, include both the purchase of beer and diapers. Since beer is included in 87.5% of diaper purchases, that indicates a link between diapers and beer. Figure 3. This theoretical example of diaper and beer purchases indicates that diapers and beer are bought together 87.5% of the time. History of association rules Association rule mining was defined in the 1990s when computer scientists Rakesh Agrawal, Tomasz Imieliński and Arun Swami used an algorithm to find relationships between items based on data from point-of-sale systems. Applying the algorithms to supermarkets, the three researchers discovered links between different items purchased and coined the term association rules, referring to information that helps predict the likelihood of different products being purchased together. The concepts behind association rules can be traced back earlier than this work. For retailers, association rule mining offered a way to better understand customer purchase behaviors. Because of its retail origins, association rule mining
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